Information Sciences 181(2011)1503-1516 Contents lists available at Science Direct Information sciences ELSEVIER journalhomepage:www.elsevier.com/locate/ins A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0 Jesus Serrano-Guerrero Enrique Herrera-Viedma, Jose A Olivas Andres Cerezo francisco P. romero a echnologies and Systems, University of Castilla-La Mancha, 13071 Ciudad ReaL Spain b Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain ARTICLE IN FO A BSTRACT Nowadays Digital Libraries 2.0 are mainly based on the interaction between users through collaborative applications such as wikis, blogs, etc or new possible paradigms like the d in revised form 17 December 2010 1 January 2011 vaves proposed by Google. This new concept, the wave, represents a common space where online 9 January 2011 resources and users can work together. The problem arises when the number of resources In this case a fuzzy linguistic recommender system based on the Google Wave capabilities ender system is proposed as tool for communicating researchers interested in common research line The system allows the creation of a common space by means a wave as a way of collabo- rating and exchanging ideas between several researchers interested in the same topic. In 2-Tuple fuzzy linguistic modeling addition, the system suggests, in an automatic way, several researchers and useful esources for each wave. These recommendations are computed following several previ- usly defined preferences and characteristics by means of fuzzy linguistic labels. Thus he system facilitates the possible collaborations between multi-disciplinar researchers Ind recommends complementary resources useful for the interaction. In order to test he effectiveness of the proposed system, a prototype of the system has been developed and tested with several research groups from the same university achieving successful e 2011 Elsevier Inc. All rights re Digital information allows the storage, access and transmission of millions of resources in an easy way but at the same time this fact involves problems for finding the suitable information. This problem is present in digital libraries Digital libraries are an extension of the classic libraries where information about different topics can be found easily, all available information is accessible through the Web[ 44. The apparition of digital libraries has changed the perception of traditional libraries [31]. Digital libraries can be focused on different contexts. In our case, we are especially interested in the University Digital Libraries (UDL). These kinds of libraries store information about books, electronic papers, electronic journals or official dailies [42, 47 and user profiles. The advent of University Digital Libraries meant a change in the life of the researchers, the amount of information avail- able grew amazingly and the necessary time to access to that information was considerably reduced. However the continued Corresponding author. Tel:+34 651504322 doi:10.1016ins201101.012
A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0 Jesus Serrano-Guerrero a,⇑ , Enrique Herrera-Viedma b , Jose A. Olivas a , Andres Cerezo a , Francisco P. Romero a aDepartment of Information Technologies and Systems, University of Castilla-La Mancha, 13071 Ciudad Real, Spain bDepartment of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain article info Article history: Received 19 February 2010 Received in revised form 17 December 2010 Accepted 1 January 2011 Available online 9 January 2011 Keywords: Recommender system University Digital Library Google wave 2-Tuple fuzzy linguistic modeling abstract Nowadays Digital Libraries 2.0 are mainly based on the interaction between users through collaborative applications such as wikis, blogs, etc. or new possible paradigms like the waves proposed by Google. This new concept, the wave, represents a common space where resources and users can work together. The problem arises when the number of resources and users is high, then tools for assisting the users in their information needs are necessary. In this case a fuzzy linguistic recommender system based on the Google Wave capabilities is proposed as tool for communicating researchers interested in common research lines. The system allows the creation of a common space by means a wave as a way of collaborating and exchanging ideas between several researchers interested in the same topic. In addition, the system suggests, in an automatic way, several researchers and useful resources for each wave. These recommendations are computed following several previously defined preferences and characteristics by means of fuzzy linguistic labels. Thus the system facilitates the possible collaborations between multi-disciplinar researchers and recommends complementary resources useful for the interaction. In order to test the effectiveness of the proposed system, a prototype of the system has been developed and tested with several research groups from the same university achieving successful results. 2011 Elsevier Inc. All rights reserved. 1. Introduction Digital information allows the storage, access and transmission of millions of resources in an easy way but at the same time this fact involves problems for finding the suitable information. This problem is present in digital libraries. Digital libraries are an extension of the classic libraries where information about different topics can be found easily, all available information is accessible through the Web [44]. The apparition of digital libraries has changed the perception of traditional libraries [31]. Digital libraries can be focused on different contexts. In our case, we are especially interested in the University Digital Libraries (UDL). These kinds of libraries store information about books, electronic papers, electronic journals or official dailies [42,47] and user profiles. The advent of University Digital Libraries meant a change in the life of the researchers, the amount of information available grew amazingly and the necessary time to access to that information was considerably reduced. However the continued 0020-0255/$ - see front matter 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ins.2011.01.012 ⇑ Corresponding author. Tel.: +34 651504322. E-mail address: jesus.serrano@uclm.es (J. Serrano-Guerrero). Information Sciences 181 (2011) 1503–1516 Contents lists available at ScienceDirect Information Sciences journal homepage: www.elsevier.com/locate/ins
1504 J. Serrano-Guerrero et al Information Sciences 181 (2011)1503-1516 nformation 2.0 N-way information ow Nonlinear Participatory Interactive Librarian 2.0 Collaborative Fig. 1. Library 2.0. development of new technologies has allowed the appearance of new paradigms: this is the case of Web 2.0 and conse- quently the appearance of Library 2.0. o The first person who used the term Web 2.0 was Dale Dougherty from the company O Reilly Media in 2004 and from that oment, Tim O'Reilly [37] started to use that term in his conferences to refer to the new developments that the Web The precise definition of Web 2.0 is not clear. Many definitions can be found but the researchers are still discussing the definitive definition. It is not clear if the Web 2.0 is a new paradigm or simply a natural evolution of the current Web. Web 2.0 is based on the user as the main figure who is capable of creating, modifying and publishing the content of the Web pages in collaboration with other users. The user is able to interact in simplified way with the applications because they are very lightweight, and it is not required to be an expert in computer science to write your own content in applications such as blogs, wikis, social networks, etc. Many new services 2.0 are appearing everyday: Facebook, Flickr, Wikipedia and Blogspot are some clear examples of this fact. The continued development of new and innovative applications involves the appearance of new paradigms, such as in the case of Google Wave, a new tool which is capable of encapsulating typical functions from other Web applications such as RSS, blogs, chats, wikis, social networks, etc. The application of the capabilities of this new technology to the UDLs is one he objectives of this work in order to extend the concept of Library 2.0. The first person who used the term Library 2.0 was Casey [12] and since that moment many related works have emerged [15,48, 50. Xu 51 depicted a model (see Fig. 1)of the Library 2.0 based on three components, (i)the information. (ii) the users and (iii)the librarians. He summarizes several applications based on Web 2.0 tools(blogs, RSS, tagging, wikis, social networks, and podcasts) applied to Academic libraries and this is the objective of this work as well, the application of the Google wave technology to develop a recommender system that will suggest users and digital resources for collaborative purposes between the users of a University Digital Library, specially the researchers. This system allows the reduction of the necessary time to find col- laborator and information about digital resources depending on the user needs. An example of an application of the system would be when several research groups want to request a European project. These research groups have decided to collaborate for research purposes and they request a common environment(a wave) from the university staff. They are the first members of the wave and for example the official announcement and other re- lated documents are the first resources of the wave but it is necessary to find new partners and old documents about the announcements from past years, etc. That is the moment in which the recommender system suggests and relevant resources from the library to achieve the collaborative objectives of the wave. The proposed recommender system is mainly based on Fuzzy Logic [18, 21] which has been used successfully in other pre- vious approaches [14, 23, 40, 41 The rest of the paper is organized as follows. Section 2 presents the preliminaries of this work: Google Wave, Recom- lender Systems and Fuzzy Linguistic Modeling. Section 3 presents the architecture and the main characteristics of the pro- posed fuzzy linguistic recommender system. Section 4 presents the results of an experiment using this system and finally ome conclusions and future works are pointed out. 2. Foundations 2.1. Google wave Web 2.0 proposes the use of many new applications with collaborative purposes, now the interaction between users is one of the main points of the information and communication technologies. Following this idea google wave has introduced a new communication and collaboration platform built around hosted conversations called waves. The wave model enables https://wavegoogle.com
development of new technologies has allowed the appearance of new paradigms; this is the case of Web 2.0 and consequently the appearance of Library 2.0. The first person who used the term Web 2.0 was Dale Dougherty from the company O’Reilly Media in 2004 and from that moment, Tim O’Reilly [37] started to use that term in his conferences to refer to the new developments that the Web is undergoing. The precise definition of Web 2.0 is not clear. Many definitions can be found but the researchers are still discussing the definitive definition. It is not clear if the Web 2.0 is a new paradigm or simply a natural evolution of the current Web. Web 2.0 is based on the user as the main figure who is capable of creating, modifying and publishing the content of the Web pages in collaboration with other users. The user is able to interact in simplified way with the applications because they are very lightweight, and it is not required to be an expert in computer science to write your own content in applications such as blogs, wikis, social networks, etc. Many new services 2.0 are appearing everyday; Facebook, Flickr, Wikipedia and Blogspot are some clear examples of this fact. The continued development of new and innovative applications involves the appearance of new paradigms, such as in the case of Google Wave,1 a new tool which is capable of encapsulating typical functions from other Web applications such as RSS, blogs, chats, wikis, social networks, etc. The application of the capabilities of this new technology to the UDLs is one the objectives of this work in order to extend the concept of Library 2.0. The first person who used the term Library 2.0 was Casey [12] and since that moment many related works have emerged [15,48,50]. Xu [51] depicted a model (see Fig. 1) of the Library 2.0 based on three components, (i) the information, (ii) the users and (iii) the librarians. He summarizes several applications based on Web 2.0 tools (blogs, RSS, tagging, wikis, social networks, and podcasts) applied to Academic Libraries and this is the objective of this work as well, the application of the Google Wave technology to develop a recommender system that will suggest users and digital resources for collaborative purposes between the users of a University Digital Library, specially the researchers. This system allows the reduction of the necessary time to find collaborators and information about digital resources depending on the user needs. An example of an application of the system would be when several research groups want to request a European project. These research groups have decided to collaborate for research purposes and they request a common environment (a wave) from the university staff. They are the first members of the wave and for example the official announcement and other related documents are the first resources of the wave, but it is necessary to find new partners and old documents about the announcements from past years, etc. That is the moment in which the recommender system suggests new participants and relevant resources from the library to achieve the collaborative objectives of the wave. The proposed recommender system is mainly based on Fuzzy Logic [18,21] which has been used successfully in other previous approaches [14,23,40,41]. The rest of the paper is organized as follows. Section 2 presents the preliminaries of this work: Google Wave, Recommender Systems and Fuzzy Linguistic Modeling. Section 3 presents the architecture and the main characteristics of the proposed fuzzy linguistic recommender system. Section 4 presents the results of an experiment using this system and finally some conclusions and future works are pointed out. 2. Foundations 2.1. Google wave Web 2.0 proposes the use of many new applications with collaborative purposes, now the interaction between users is one of the main points of the information and communication technologies. Following this idea Google Wave has introduced a new communication and collaboration platform built around hosted conversations called waves. The wave model enables Fig. 1. Library 2.0. 1 https://wave.google.com. 1504 J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516
J. Serrano-Guerrero et aL Information Sciences 181(2011)1503-1516 1505 people to communicate and work together in new and more effective ways. The system is based on the google Wave Fed- ration Protocol for sharing waves between wave providers According to the google development team, "Google Wave is an online tool for real-time communication and collabora- tion. a wave can be both a conversation and a document where people can discuss and work together using richly formatted text, photos, videos, maps, and more Therefore this new concept ofwave can be understood as a new application which merges capabilities from blogs, chats. wikis. etc on the platform. The wave can be understood as a conversation or a document where several participants n publish messages or edit the existing ones and see what, when and who edited each part of the wave Google wave has many innovative features such as (])real-time: you can see what someone else is typing. iiembedda- bility: waves can be embedded on any blog or website, (iii) applications and extensions: developers can build their own applications within waves, (iv)playback: the user can playback any part of the wave to see what was said, (v)open source, vi)wiki functionality: anything written within a Google wave can be edited by anyone else, because all of the conversations thin the platform are shared and therefore the users can correct information, append new information, comment the exist ng information within a developing conversation, (vii) drag-and-drop file sharing: the user can drag a file and drop it inside the wave and everyone will have access, etc. One of the most important Google Wave characteristics is the extensions. An extension is a mini-application that works within a wave. There are two main types of extensions: Gadgets and robots Gadgets are specific to individual waves, rather than to specific users, the gadget belongs to everyone within the wave Some of the gadgets already built include games for several participating players, maps for several navigating users on the same Google Map, etc. Robots are the other type of Google Wave extension. Robots are like having another person within a Google Wave con- ersation. Robots can carry out several actions such as the modification of information in waves, interaction with users, com- munication with the waves of others and the insertion of information from external sources the behavior of each robot is programmed and they can be working in the background while users are writing within the wave. 2. 2. Recommender systems Recommender systems are one of the most studied current research lines today however. despite all of the achieved ad- vances, the current generation of recommender systems still requires further improvement to make recommendations more effective and applicable to a broader range of applications [1. A recommendation system can be considered as a system which provides personalized information services in different to characterize recommender systems [38]. depending on how the system is.*381. It is necessary to study four dimensions ways reducing information overload taking into account the user preferences · modeled and designed targeted(to an individual, group, or topic). built an ed (online vs offline ). Manyclassifications can be found [43 ]. for example, based on how recommendations are made. Recommender systems can be classified into three main categories [3].(i)Content-based recommender he recommendations are based on an item chosen by the user in the past, (ii)Collaborative recommender systems: the recommendations are based on items hosen by other users with similar preferences to our user, and (iii) Hybrid recommender systems: this approach combines the two previous methods. The latter category can be sub-classified (i)implementing collaborative and content-based approaches in a separate way nd combining their results. (ii) incorporating some content-based characteristics into a collaborative method, (iii) incorpo- rating some collaborative characteristics into a content-based method, and (iv) developing a method capable of incorporat ing both content-based and collaborative characteristics [1]. Recommender systems have an underlying social element and consequently involve user modeling, i. e the representa tion of user preferences. This information can be directly provided by the user or inferred from user data stored in the system [3849 These kinds of systems have been applied successfully to different domains such as e-commerce [10, 13, 30], University D igital Libraries [35, 39, 41, movies recommendations and TV programms[2,4, 34], technology ti nsference[40, service loca- tion [46, education [11. news 29.etc. 2.3. 2-Tuple fuzzy linguistic approach The quantitative assessment of the different aspects of the real world is not always due to th precision of the underlying knowledge. For this reason a linguistic approach can be a mo resting alternative instead of the use of numerical values. The fuzzy linguistic approach is based on the representation
people to communicate and work together in new and more effective ways. The system is based on the Google Wave Federation Protocol for sharing waves between wave providers. According to the Google development team,‘‘Google Wave is an online tool for real-time communication and collaboration. A wave can be both a conversation and a document where people can discuss and work together using richly formatted text, photos, videos, maps, and more’’. Therefore this new concept of ‘wave’ can be understood as a new application which merges capabilities from blogs, chats, wikis, etc. on the same platform. The wave can be understood as a conversation or a document where several participants can publish messages or edit the existing ones and see what, when and who edited each part of the wave. Google Wave has many innovative features such as (i) real-time: you can see what someone else is typing, (ii) embeddability: waves can be embedded on any blog or website, (iii) applications and extensions: developers can build their own applications within waves, (iv) playback: the user can playback any part of the wave to see what was said, (v) open source, (vi) wiki functionality: anything written within a Google Wave can be edited by anyone else, because all of the conversations within the platform are shared and therefore the users can correct information, append new information, comment the existing information within a developing conversation, (vii) drag-and-drop file sharing: the user can drag a file and drop it inside the wave and everyone will have access, etc. One of the most important Google Wave characteristics is the extensions. An extension is a mini-application that works within a wave. There are two main types of extensions: Gadgets and Robots. Gadgets are specific to individual waves, rather than to specific users, the gadget belongs to everyone within the wave. Some of the gadgets already built include games for several participating players, maps for several navigating users on the same Google Map, etc. Robots are the other type of Google Wave extension. Robots are like having another person within a Google Wave conversation. Robots can carry out several actions such as the modification of information in waves, interaction with users, communication with the waves of others, and the insertion of information from external sources. The behavior of each robot is programmed and they can be working in the background while users are writing within the wave. 2.2. Recommender systems Recommender systems are one of the most studied current research lines today, however, despite all of the achieved advances, the current generation of recommender systems still requires further improvement to make recommendations more effective and applicable to a broader range of applications [1]. A recommendation system can be considered as a system which provides personalized information services in different ways reducing information overload taking into account the user preferences [7,38]. It is necessary to study four dimensions to characterize recommender systems [38], depending on how the system is: modeled and designed, targeted (to an individual, group, or topic), built and maintained (online vs. offline). Manyclassifications can be found [43], for example, based on how recommendations are made. Recommender systems can be classified into three main categories [3], (i) Content-based recommender systems: the recommendations are based on an item chosen by the user in the past, (ii) Collaborative recommender systems: the recommendations are based on items chosen by other users with similar preferences to our user, and (iii) Hybrid recommender systems: this approach combines the two previous methods. The latter category can be sub-classified (i) implementing collaborative and content-based approaches in a separate way and combining their results, (ii) incorporating some content-based characteristics into a collaborative method, (iii) incorporating some collaborative characteristics into a content-based method, and (iv) developing a method capable of incorporating both content-based and collaborative characteristics [1]. Recommender systems have an underlying social element and consequently involve user modeling, i.e., the representation of user preferences. This information can be directly provided by the user or inferred from user data stored in the system [38,49]. These kinds of systems have been applied successfully to different domains such as e-commerce [10,13,30], University Digital Libraries [35,39,41], movies recommendations and TV programms [2,4,34], technology transference [40], service location [46], education [11], news [29], etc. 2.3. 2-Tuple fuzzy linguistic approach The quantitative assessment of the different aspects of the real world is not always possible due to the vagueness or imprecision of the underlying knowledge. For this reason a linguistic approach can be a more interesting alternative instead of the use of numerical values. The fuzzy linguistic approach is based on the representation of qualitative aspects as linguistic J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516 1505
J. Serrano-Guerrero et al Information Sciences 181 (2011)1503-1516 alues by means of linguistic variables 52. Its application has been successful to different problems such as information retrieval [5,6, 22-24, 28, recommender systems [40, 41]. quality evaluation [26, 27, 36 decision making 8,9, 17.3 The 2-tuple FLM [20 is a continuous model of representation of information which reduces the loss of typical of other fuzzy linguistic approaches (classical and ordinal [ 16, 52)). To define it we have to establish representation model and the 2-tuple computational model to represent and aggregate the linguistic respectively. Let S=(So, J be a linguistic term set with odd cardinality, where the middle term an indifference value and the rest of the terms are symmetrically related to it. We assume that the semantics of the labels are given by means of tri- ngular membership functions and we consider that all terms are distributed on a scale in which a total order is defined, S, a2 then(Sk, M) is bigger than(s,2) 3. Aggregation operators. The aggregation of information consists of obtaining a value that summarizes a set of values, herefore, the result of the aggregation of a set of 2-tuples must be a 2-tuple. In the literature we can find many aggre- tion operators which allow us to combine the information according to different criteria Using functions A and A hat transform without loss of information numerical values into linguistic 2-tuples and vice versa, any of the existing gregation operator can be easily extended for dealing with linguistic 2-tuples. Some examples are Definition 3(Arithmetic mean). Let x=((r1, a1),... (n, Mn)) be a set of linguistic 2-tuples, the 2-tuple arithmetic mean x is omputed as xn2x)…-(2(0)-4(S) Definition 4(Weighted average operator). Let x=((r1, 1)..... (rn, an) be a set of linguistic 2-tuples and w=(wi,., wn) be their associated weights. The 2-tuple weighted average xis:
values by means of linguistic variables [52]. Its application has been successful to different problems such as information retrieval [5,6,22–24,28], recommender systems [40,41], quality evaluation [26,27,36], decision making [8,9,17,32,53], etc. The 2-tuple FLM [20] is a continuous model of representation of information which reduces the loss of information typical of other fuzzy linguistic approaches (classical and ordinal [16,52]). To define it we have to establish the 2-tuple representation model and the 2-tuple computational model to represent and aggregate the linguistic information respectively. Let S = {s0,..., sg} be a linguistic term set with odd cardinality, where the middle term represents an indifference value and the rest of the terms are symmetrically related to it. We assume that the semantics of the labels are given by means of triangular membership functions and we consider that all terms are distributed on a scale in which a total order is defined, si 6 sj () i 6 j. In this fuzzy linguistic context, a symbolic method [19,16] aggregating linguistic information obtains a value b 2 [0,g] and b R {0,...,g} then an approximation function is used to express the result in S. Definition 1. Let b be the result of an aggregation of the indexes of a set of labels assessed in a linguistic term set S, i.e., the result of a symbolic aggregation operation, b 2 [0,g]. Let i = round(b) and a = b i be two values, such that, i 2 [0,g] and ai 2 [.5, .5) then a is called a Symbolic Translation [20]. The 2-tuple fuzzy linguistic approach is developed from the concept of symbolic translation by representing the linguistic information by means of 2-tuples (si,ai), si 2 S and ai 2 [.5,.5): si represents the linguistic label of the information, and ai is a numerical value expressing the value of the translation from the original result b to the closest index label, i, in the linguistic term set (si 2 S). This model defines a set of transformation functions between numeric values and 2-tuples. Definition 2. Let S = {s0, s1,..., sg} be a linguistic term set and b 2 [0,g] a value representing the result of a symbolic aggregation operation, then the 2-tuple that expresses the equivalent information to b is obtained with the following function [20]: D : ½0; g ! S ½0:5; 0:5Þ; DðbÞ¼ðsi; aÞ; with si i ¼ roundðbÞ; a ¼ b i ai 2 ½:5; :5Þ; where round() is the usual round operation, si has the closest index label to ‘‘b’’ and ‘‘a’’ is the value of the symbolic translation. For all D there exists D1 (si,a) = i + a. On the other hand, it is obvious that the conversion of a linguistic term into a linguistic 2-tuple consists of adding a symbolic translation value of 0: si 2 S ) (si,0). The computational model is defined by presenting the following operators: 1. Negation operator: Neg(si,a) = D(g D1 (si,a)) 2. Comparison of 2-tuples (sk,a1) and (sl,a2): if k < l then (sk,a1) is smaller than (sl,a2) if k = l then (a) if a1 = a2 then (sk,a1) and (sl,a2) represent the same informations (b) if a1 6 a2 then (sk,a1) is smaller than (sl,a2) (c) if a1 P a2 then (sk,a1) is bigger than (sl,a2) 3. Aggregation operators. The aggregation of information consists of obtaining a value that summarizes a set of values, therefore, the result of the aggregation of a set of 2-tuples must be a 2-tuple. In the literature we can find many aggregation operators which allow us to combine the information according to different criteria. Using functions D and D1 that transform without loss of information numerical values into linguistic 2-tuples and vice versa, any of the existing aggregation operator can be easily extended for dealing with linguistic 2-tuples. Some examples are: Definition 3 (Arithmetic mean). Let x = {(r1,a1),..., (rn,an)} be a set of linguistic 2-tuples, the 2-tuple arithmetic mean xe is computed as: xe ½ðr1; a1Þ; ... ;ðrn; anÞ ¼ D Xn i¼1 1 n D1 ðri; aiÞ ! ¼ D 1 n Xn i¼1 bi !: Definition 4 (Weighted average operator). Let x = {(r1,a1),..., (rn,an)} be a set of linguistic 2-tuples and W = {w1,...,wn} be their associated weights. The 2-tuple weighted average xw is: 1506 J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516
J. Serrano-Guerrero et aL Information Sciences 181(2011)1503-1516 1507 (r1,x1)…(m,xn)=△ ∑1A()*w=△(∑w 1B1*W efinition 5(Linguistic weighted average operator). Let x=((ra,,. (n,an) be a set of linguistic 2-tuples and I(w1, a),.... (wn, aw)) be their linguistic 2-tuple associated we eights. The 2-tuple linguistic weighted average xy is: R"[(r1,a1,w1,x")…,(n,x1),w,z7) with B=△-(r,x)andw2=△-(w,x) In any fuzzy linguistic approach, an important parameter to determine is the"granularity of uncertainty, i.e., the cardi nality of the linguistic term set S. According to the uncertainty degree that an expert qualifying a phenomenon has on it, the linguistic term set chosen to provide his knowledge will have more or less terms. When different experts have different uncertainty degrees on the phenomenon then several linguistic term sets with a different granularity of uncertainty are n esry[21,25,33 The use of different label sets to assess information is also necessary when an expert has to assess different concepts, as for example it happens in information retrieval problems, to evaluate the importance of the query terms and the importance of the retrieved documents [27 In such situations, we need tools to manage multi-granular linguistic information. In[22, 28 a multi-granular 2-tuple FLm based on the concept of linguistic hierarchy is proposed A Linguistic Hierarchy, LH, is a set of levels I(t, n(t)), i.., LH= U(t, n(t)), where each level t is a linguistic term set with a different granularity n(t) from the remaining of levels of the hierarchy. the levels are ordered according to their granularity e, a level t+ 1 provides a linguistic refinement of the previous level t We can define a level from its predecessor level as I(t, n(t))-(t+ 1. 2*n(t)-1) table 1 shows the granularity needed in each linguistic term set of the level t depending on the value n(t) defined in the first level (3 and 7, respectively A graphical example of a linguistic hierarchy is shown in Fig. 2. Herrera [21] demonstrated that linguistic hierarchies are useful to represent multi-granular linguistic information and allow the combination of multi-granular linguistic information without loss of information. To do this, a family of transfor mation functions between labels from different levels was defined transformation function between a 2-tuple that belongs to level t and another 2-tuple in level t +t is defined.5i-i.The Definition 6. Let LH=Ur(t, n(t)be a linguistic hierarchy whose linguistic term sets are denoted as sn(=(som) TF=lt,n(t)→l(t,n(t) n(t)-1 Table 1 Linguistic hierarchies. Level 1 Level 2 Mr, n(r) I(2.5) KL, n(t) l(2,13) Fig. 2. Linguistic hierarchy of 3. 5 and 9 labels
xw½ðr1; a1Þ; ... ;ðrn; anÞ ¼ D Pn i¼1D1 ðri P ; aiÞ wi n i¼1wi ! ¼ D Pn i¼1 P bi wi n i¼1wi : Definition 5 (Linguistic weighted average operator). Let x = {(r1,a1),..., (rn,an)} be a set of linguistic 2-tuples and W ¼ fðw1; aw 1 Þ; ... ;ðwn; aw n Þg be their linguistic 2-tuple associated weights. The 2-tuple linguistic weighted average xw l is: xw ððr1; a1Þ;ðw1; aw 1 ÞÞ; ... ; ðr1; a1Þ; w1; aw 1 ¼ D Pn i¼1 P bi bwi n i¼1bwi !; with bi ¼ D1 ðri; aiÞ and bwi ¼ D1 wi; aw i : 2.4. The multi-granular fuzzy linguistic modeling In any fuzzy linguistic approach, an important parameter to determine is the ‘‘granularity of uncertainty’’, i.e., the cardinality of the linguistic term set S. According to the uncertainty degree that an expert qualifying a phenomenon has on it, the linguistic term set chosen to provide his knowledge will have more or less terms. When different experts have different uncertainty degrees on the phenomenon, then several linguistic term sets with a different granularity of uncertainty are necessary [21,25,33]. The use of different label sets to assess information is also necessary when an expert has to assess different concepts, as for example it happens in information retrieval problems, to evaluate the importance of the query terms and the importance of the retrieved documents [27]. In such situations, we need tools to manage multi-granular linguistic information. In [22,28] a multi-granular 2-tuple FLM based on the concept of linguistic hierarchy is proposed. A Linguistic Hierarchy, LH, is a set of levels l(t,n(t)), i.e., LH = S tl(t,n(t)), where each level t is a linguistic term set with a different granularity n(t) from the remaining of levels of the hierarchy. The levels are ordered according to their granularity, i.e., a level t + 1 provides a linguistic refinement of the previous level t. We can define a level from its predecessor level as: l(t,n(t)) ? l(t + 1,2⁄n(t) 1). Table 1 shows the granularity needed in each linguistic term set of the level t depending on the value n(t) defined in the first level (3 and 7, respectively). A graphical example of a linguistic hierarchy is shown in Fig. 2. Herrera [21] demonstrated that linguistic hierarchies are useful to represent multi-granular linguistic information and allow the combination of multi-granular linguistic information without loss of information. To do this, a family of transformation functions between labels from different levels was defined: Definition 6. Let LH = S tl(t,n(t)) be a linguistic hierarchy whose linguistic term sets are denoted as SnðtÞ ¼ fs nðtÞ 0 ; ... ; s nðtÞ t1g. The transformation function between a 2-tuple that belongs to level t and another 2-tuple in level t 0 – t is defined as: TFt t0 ¼ lðt; nðtÞÞ ! lðt 0 ; nðt 0 ÞÞ; TFt t0 s nðtÞ i ; anðtÞ ¼ D D1 ðs nðtÞ i ; anðtÞ Þðnðt 0 Þ 1Þ nðtÞ 1 !: Table 1 Linguistic hierarchies. Level 1 Level 2 Level 3 l(t,n(t)) l(1, 3) l(2,5) l(3, 9) l(t,n(t)) l(1, 7) l(2,13) Fig. 2. Linguistic hierarchy of 3, 5 and 9 labels. J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516 1507
1508 J. Serrano-Guerrero et al Information Sciences 181 (2011)1503-1516 As it was pointed out in [21 this family of transformation functions is bijective. This result guarantees that the transforma- tions between levels of a linguistic hierarchy are carried out without loss of information. To define the computational model we select a level to make the information uniform(for instance, the greatest granularity level)and then we can use the oper ators defined in the 2-tuple FLM 3. Recommender system for the dissemination of information in University Digital Libraries University Digital Libraries allow storing a large amount of digital resources to be accessed by the university communit The researchers may be interested in several topics but they are not expert in every topic that they are interested in. For this reason, a platform is necessary in order to interact with other experts that can support them. The Google waves are useful ool for them since they can access information written by other users, specially useful for new participants, or they can start the interaction with other experts in order to find collaborators or simply to find the answers to their questions. Indeed, the information stored in the waves can be accessed as past experiences in order to know the steps followed in other collabo- ations and to avoid past errors. Therefore the Google waves can be an interesting collaborative tool but it is necessary to have a process which suggests information about people and resources that can participate in the wave because the wave creator/administrator does not know all the resources or people registered into the library that can be useful for the wave. The objective of this section is the description of a recommender system that is able to inform the user about the existence of an interesting wave and to inform the wave administrator about the existence of useful resources that can be included in the wave The system differentiates two kinds of users in the system: administrators and users. Both administrators and users are researchers who use the University Digital Library but can play different roles. The administrators are experts interested in finding collaborators for research purposes and hence they are in charge of defining the characteristics of a wave. Moreover they are in charge of inserting the resources because they know best the characteristics of each resource On the other hand, users receive requests for collaboration and decide if they want to participate in the wave. therefore the activity of a researcher consists only in defining his profile and participating in the chosen waves. The users of the wave an propose to the administrator the inclusion of new resources that they have and know would be useful for the wave users but the administrator is who ultimately decides if the resource is inserted or not. Our system presents three main recommendations depending on the action that is carried out: Insertion of a user. When a user is logged into the system he receives information about the waves which he might be Insertion of a digital resource. When a digital resource is inserted into the system, the administrators of the system receive a recommendation about which waves can include the resource Insertion of a wave. when a new wave is created the system searches people and resources that may be useful for the The insertion of each element (users, resources and waves) consists in filling out a form which contains the information that characterizes each element That information is described in the following subsection. Each form presents a set of label ts(S1, S2,... that allows the assessment of the different concepts managed by system. These label sets are chosen from the label sets that compose a Linguistic Hierarchy. It is necessary to remark that the number of levels of the Linguistic Hierarchy restricts the number of different label sets, therefore two label sets Si and Sy can represent the label set of the Linguistic Hier archy however the interpretation of both(SSy)can be different. In the proposed system it is necessary to distinguish four concepts that can be assessed Importance degree (Sn)of a discipline with respect to the scope of the resource the user preferences or the scope of the Importance degree(S,)of a wave according to user opinion. Importance degree(S3)of a resource for a wave. Importance degree(S4)of a resource according to the opinion of the person who required the insertion of that resource llowing the linguistic hierarchy shown in Fig. 2, in our system we use level 2 (5 labels )to assign importance degree (S1=S)and level 3(9 labels)to assign importance degrees(S2=S3=S4=S). Using this lh the linguistic terms in each level
As it was pointed out in [21] this family of transformation functions is bijective. This result guarantees that the transformations between levels of a linguistic hierarchy are carried out without loss of information. To define the computational model, we select a level to make the information uniform (for instance, the greatest granularity level) and then we can use the operators defined in the 2-tuple FLM. 3. Recommender system for the dissemination of information in University Digital Libraries University Digital Libraries allow storing a large amount of digital resources to be accessed by the university community. The researchers may be interested in several topics but they are not expert in every topic that they are interested in. For this reason, a platform is necessary in order to interact with other experts that can support them. The Google waves are useful tool for them since they can access information written by other users, specially useful for new participants, or they can start the interaction with other experts in order to find collaborators or simply to find the answers to their questions. Indeed, the information stored in the waves can be accessed as past experiences in order to know the steps followed in other collaborations and to avoid past errors. Therefore the Google waves can be an interesting collaborative tool but it is necessary to have a process which suggests information about people and resources that can participate in the wave because the wave creator/administrator does not know all the resources or people registered into the library that can be useful for the wave. The objective of this section is the description of a recommender system that is able to inform the user about the existence of an interesting wave and to inform the wave administrator about the existence of useful resources that can be included in the wave. 3.1. Roles The system differentiates two kinds of users in the system: administrators and users. Both administrators and users are researchers who use the University Digital Library but can play different roles. The administrators are experts interested in finding collaborators for research purposes and hence they are in charge of defining the characteristics of a wave. Moreover they are in charge of inserting the resources because they know best the characteristics of each resource. On the other hand, users receive requests for collaboration and decide if they want to participate in the wave, therefore the activity of a researcher consists only in defining his profile and participating in the chosen waves. The users of the wave can propose to the administrator the inclusion of new resources that they have and know would be useful for the wave users, but the administrator is who ultimately decides if the resource is inserted or not. 3.2. Recommendations Our system presents three main recommendations depending on the action that is carried out: Insertion of a user. When a user is logged into the system he receives information about the waves which he might be interested in. Insertion of a digital resource. When a digital resource is inserted into the system, the administrators of the system receive a recommendation about which waves can include the resource. Insertion of a wave. When a new wave is created, the system searches people and resources that may be useful for the waves purposes. The insertion of each element (users, resources and waves) consists in filling out a form which contains the information that characterizes each element. That information is described in the following subsection. Each form presents a set of label sets (S1,S2,...) that allows the assessment of the different concepts managed by system. These label sets are chosen from the label sets that compose a Linguistic Hierarchy. It is necessary to remark that the number of levels of the Linguistic Hierarchy restricts the number of different label sets, therefore two label sets Si and Sj can represent the label set of the Linguistic Hierarchy however the interpretation of both (Si,Sj) can be different. In the proposed system it is necessary to distinguish four concepts that can be assessed: Importance degree (S1) of a discipline with respect to the scope of the resource, the user preferences or the scope of the wave. Importance degree (S2) of a wave according to user opinion. Importance degree (S3) of a resource for a wave. Importance degree (S4) of a resource according to the opinion of the person who required the insertion of that resource. Following the linguistic hierarchy shown in Fig. 2, in our system we use level 2 (5 labels) to assign importance degree (S1 = S5 ) and level 3 (9 labels) to assign importance degrees (S2 = S3 = S4 = S9 ). Using this LH the linguistic terms in each level are: 1508 J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516
J. Serrano-Guerrero et aL Information Sciences 181(2011)1503-1516 1509 B/y№H三理理票,C鲁m ouandExampleengined.com welcome to wavesengin Resources Subject Linguistic label User Preferences Agncultural and Biolog cal sciences Null Agncumural and Biclogica SciencesLow Fig. 3. Interface for inserting a linguistic value for each discipline S=(bo=Null =N, b=Low=L, b2=Medium =M, b 3= High=H, ba=Total=T). S=(co=Null =N, c1=Very-Low =VL, C2=Low=L, C3=More_Less_Low=MLL, Ca=Medium More_Less_high= MLH. C6= High=H, c,= very_High= VH, cs=Total=T ns with respect to the 26 ca gories proposed by ScienceDirect(see Fig 3): (i)Business, Management and Accounting.(i)Chemistry. (ii)Computer Sci- ence, etc. The comparisons between user and user, resource and resource, resource and etc, are based on the use of measures such as the cosine measure. In this case the standard cosine will be the chosen measure based on linguistic values 0(V1,V2)=4g 1.xn)×△-(a2k,xak) where g is the granularity of the used term set, n is the number of terms used to define the vectors (i.e. the number of dis- ciplines)and(oik, apik) is the linguistic value of term k in the user, resource or wave vector Va With this similarity measure we btain a linguistic value in S1 to assess the similarity among two resources, two users, or a resource and a wave, etc. 3.3. Information representation The administrators of the system are people in charge of defining the waves that will exist in the digital library as well ne characteristics of each resource, whereas the user is who inserts his own personal data and main preferences. All nec- essary data to describe each element are discussed below. 3.3.1. Resources Different resources can be found in digital libraries such as books, electronic papers, electronic journals and official dailies [42, 47). An example could be the resources of The Stanford Digital Library Technologies Project. The resources can be classified according to the above-explained classification proposed by ScienceDirect. The adminis- rator who inserts each digital resource has 26 different disciplines to characterize the topics of each resource. The descrip- tion of each resource i is stored in a vector VR with 26 positions, one for each discipline where the administrator can assign a -tuple linguistic label bx E Sn for each position WR={WR1,WR2,…,VRa6} Therefore, each component VRy of the vector VR; indicates the 2-tuple tic importance degree of the dis with j=(1,., 26], with respect to the resource i. Thus the administrator of ive is the person in charge of indic ferent 2-tuple linguistic degrees of each resource that is incorporated i The administrator can insert a new re. source in response to a user who informs about the existence of that useful resource. http://www.info.sciencedirect.c llections 3http://diglib.stanford.edu:8091diglib/pub/resources.shtml
S5 = {b0 = Null = N,b1 = Low = L,b2 = Medium = M,b3 = High = H,b4 = Total = T}. S9 = {c0 = Null = N, c1 = Very_Low = VL, c2 = Low = L, c3 = More_Less_Low = MLL, c4 = Medium = M, c5 = More_Less_High = MLH, c6 = High = H, c7 = Very_High = VH, c8 = Total = T}. The representation of users, waves and resources is a vector [45] that describes their relations with respect to the 26 categories proposed by ScienceDirect2 (see Fig. 3): (i) Business, Management and Accounting, (ii) Chemistry, (iii) Computer Science, etc. The comparisons between user and user, resource and resource, resource and wave, etc., are based on the use of measures such as the cosine measure. In this case the standard cosine will be the chosen measure based on linguistic values: rlðV1; V2Þ ¼ D g Pn k¼1ðD1 ð#1k; a#1kÞ D1 ð#2k; a#2kÞÞ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn k¼1ðD1 ð#1k; a#1kÞÞ2 q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn k¼1ðD1 ð#2k; a#2kÞÞ2 q 0 B@ 1 CA; where g is the granularity of the used term set, n is the number of terms used to define the vectors (i.e. the number of disciplines) and (#ik,a#ik) is the linguistic value of term k in the user, resource or wave vector Vi. With this similarity measure we obtain a linguistic value in S1 to assess the similarity among two resources, two users, or a resource and a wave, etc. 3.3. Information representation The administrators of the system are people in charge of defining the waves that will exist in the digital library as well as the characteristics of each resource, whereas the user is who inserts his own personal data and main preferences. All necessary data to describe each element are discussed below. 3.3.1. Resources Different resources can be found in digital libraries such as books, electronic papers, electronic journals and official dailies [42,47]. An example could be the resources of The Stanford Digital Library Technologies Project.3 The resources can be classified according to the above-explained classification proposed by ScienceDirect. The administrator who inserts each digital resource has 26 different disciplines to characterize the topics of each resource. The description of each resource i is stored in a vector VRi with 26 positions, one for each discipline where the administrator can assign a 2-tuple linguistic label bx 2 S1 for each position: VRi ¼ fVRi1; VRi2; ... ; VRi26g: Therefore, each component VRij of the vector VRi indicates the 2-tuple linguistic importance degree of the discipline j, with j = {1,..., 26}, with respect to the resource i. Thus the administrator of the wave is the person in charge of indicating the different 2-tuple linguistic degrees of each resource that is incorporated into the wave. The administrator can insert a new resource in response to a user who informs about the existence of that useful resource. Fig. 3. Interface for inserting a linguistic value for each discipline. 2 http://www.info.sciencedirect.com/content/backfiles/collections. 3 http://diglib.stanford.edu:8091/diglib/pub/Resources.shtml. J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516 1509
1510 J. Serrano-Guerrero et al Information Sciences 181 (2011)1503-1516 Resource Recommender System Resource wave 3 Google Wave-based Digital Library Tool Fig 4 Scheme of the google wave-based recommender system for University Digital Libraries. Therefore a resource is defined by a vector VR, apart from other typical data such as a name, a description and an impor tance degree. This last value is firstly fixed by the user who suggests the resource as a member of the wave but can be refixed by the wave administrator s. This value is expressed by a 2-tuple linguistic label bx E S3. 3.3.2. User profiles Both administrators and users have to define their profile before starting to work with the system. the user has to de- scribe his preferences with respect to the same disciplines proposed for the resources in the previous subsection. The way of defining the preferences is the same, the user has to select a 2-tuple linguistic value bx E Sn for each one of the 26 positions of a vector VU. Apart from this vector, the user has to give more information such as his name, family name, email address( Google address to be inserted in the waves), nickname, password, etc. The system also stores a list of waves to which the user is subscribed and a score, a 2-tuple linguistic value bx E S2, for each wave depending on his interest in each wave. This data is provided by the user when he accepts the invitation for participation in the wave. The user also has to define a linguistic threshold value for waves (n) that indicates the minimum value required for informing him about the exis- tence of a wave 3.3.3. Waves The waves present a similar definition with respect to resources and users; a vector W where the administrator has to describe the topics of the wave(see Section 3. 2). But this defin tIon Is ommendations of the system which suggests the most appropriate users who might be interested in and the re- ources that could be interesting according to the theme of the wave
Therefore a resource is defined by a vector VR, apart from other typical data such as a name, a description and an importance degree. This last value is firstly fixed by the user who suggests the resource as a member of the wave but can be refixed by the wave administrator/s. This value is expressed by a 2-tuple linguistic label bx 2 S3. 3.3.2. User profiles Both administrators and users have to define their profile before starting to work with the system. The user has to describe his preferences with respect to the same disciplines proposed for the resources in the previous subsection. The way of defining the preferences is the same, the user has to select a 2-tuple linguistic value bx 2 S1 for each one of the 26 positions of a vector VU. Apart from this vector, the user has to give more information such as his name, family name, email address (Google address to be inserted in the waves), nickname, password, etc. The system also stores a list of waves to which the user is subscribed and a score, a 2-tuple linguistic value bx 2 S2, for each wave depending on his interest in each wave. This data is provided by the user when he accepts the invitation for participation in the wave. The user also has to define a linguistic threshold value for waves (c) that indicates the minimum value required for informing him about the existence of a wave. 3.3.3. Waves The waves present a similar definition with respect to resources and users; a vector VW with 26 positions where the administrator has to describe the topics of the wave (see Section 3.2). But this definition is completed by means of the recommendations of the system which suggests the most appropriate users who might be interested in the wave and the resources that could be more interesting according to the theme of the wave. Fig. 4. Scheme of the google wave-based recommender system for University Digital Libraries. 1510 J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516
J. Serrano-Guerrero et aL Information Sciences 181(2011)1503-1516 1511 Apart from the vector v the wave definition includes a name, a description, i.e., a short explanation of the purpose of the wave, a list of descriptive words(key-words )about the topic of the wave and a list of administrators, i.e. people in charge of creating and supervising the activity within the wave The wave administrator has to assign a linguistic threshold value for resources(a)which indicates the minimum value lat can be required by an administrator for informing him about the existence of a new interesting resource for his admin- strated waves. Indeed the user has to fix a linguistic threshold value for users(o) which determines the minimum relation- Finally the wave definition is completed by a list of users who interact with the wave and a list of resources available in the wave. Each user describes the degree of importance bx E S3 of the wave with respect to his interests as was discussed in Section 3.3. 2, and furthermore, the user who suggested the resource or the wave administrator describes the importance degree bx E S] of that resource for the wave as was discussed in Section 3.3.1 3.4. Steps of the system he proposed approach, contrary to other works 40, 41, does not suggest collaborators or resources for a particular user but recommends the activity of users and resources within specialized forums through the collaborative tool called Google Wave. a detailed explanation of the recommendation process is described in the following subsections 3.4.1. Insertion of a wave When several researchers decide that it is necessary to open a new wave in order to find people and resources for a new initiative, they communicate their desire to the library staff and then the staff members decide if there are enough research- ers for creating the wave and if the proposal is relevant. If the request is accepted, one or various researchers are selected a administrators and are in charge of inserting the first users and resources into the wave by hand because they know of ticipants interested in the wave and the resources that they have. when this process is finished the system starts with automatic recommendations. There are two possible elements to be suggested for each wave i, a set of users Ui and a set of resources R a general view of the system can be seen in Fig 4 within a digital library with many other tools. The flow of the recommendations for the wave is depicted in Fig. 5. Each user has to be registered into the system by filling out the registration forms whereas the resources are registered by the administrators of the wave filling out other form as will be discussed below. The definition of the waves is performed by one or several administrators and completed by the recommender system which associates the most relevant resources and users with the wave. The first members and resources of the wave are inserted by hand, but the rest of the process is computed automatically The internal representation of the wave i is a vector Vw which stores 2-tuple linguistic values for each category defined in Science Direct apart from the other data commented in Section 3.3.3. The system matches the wave vector wi and the vec tors VUk for each user k within the system and if the result exceeds the threshold o fixed by the wave administrator/s (oNV VUk)>o) then the system informs the user k via email about the existence of the wave i Next, users have to decide Administrator Resource vectors wave vector Matc User Fig. 5. Flow of the recommender system for the wave definition
Apart from the vector VW the wave definition includes a name, a description, i.e., a short explanation of the purpose of the wave, a list of descriptive words (key-words) about the topic of the wave and a list of administrators, i.e., people in charge of creating and supervising the activity within the wave. The wave administrator has to assign a linguistic threshold value for resources (k) which indicates the minimum value that can be required by an administrator for informing him about the existence of a new interesting resource for his administrated waves. Indeed, the user has to fix a linguistic threshold value for users (x) which determines the minimum relationship between the wave and an existing user in the system in order to consider that both share interests. Finally the wave definition is completed by a list of users who interact with the wave and a list of resources available in the wave. Each user describes the degree of importance bx 2 S3 of the wave with respect to his interests as was discussed in Section 3.3.2, and furthermore, the user who suggested the resource or the wave administrator describes the importance degree bx 2 S2 of that resource for the wave as was discussed in Section 3.3.1. 3.4. Steps of the system The proposed approach, contrary to other works [40,41], does not suggest collaborators or resources for a particular user but recommends the activity of users and resources within specialized forums through the collaborative tool called Google Wave. A detailed explanation of the recommendation process is described in the following subsections. 3.4.1. Insertion of a wave When several researchers decide that it is necessary to open a new wave in order to find people and resources for a new initiative, they communicate their desire to the library staff and then the staff members decide if there are enough researchers for creating the wave and if the proposal is relevant. If the request is accepted, one or various researchers are selected as administrators and are in charge of inserting the first users and resources into the wave by hand because they know of participants interested in the wave and the resources that they have. When this process is finished the system starts with the automatic recommendations. There are two possible elements to be suggested for each wave i, a set of users Ui and a set of resources Ri. A general view of the system can be seen in Fig. 4 within a digital library with many other tools. The flow of the recommendations for the wave is depicted in Fig. 5. Each user has to be registered into the system by filling out the registration forms whereas the resources are registered by the administrators of the wave filling out other form as will be discussed below. The definition of the waves is performed by one or several administrators and completed by the recommender system which associates the most relevant resources and users with the wave. The first members and resources of the wave are inserted by hand, but the rest of the process is computed automatically. The internal representation of the wave i is a vector VWi which stores 2-tuple linguistic values for each category defined in ScienceDirect apart from the other data commented in Section 3.3.3. The system matches the wave vector VWi and the vectors VUk for each user k within the system and if the result exceeds the threshold x fixed by the wave administrator/s (rl(VWi,VUk) > x) then the system informs the user k via email about the existence of the wave i. Next, users have to decide User insertion Resources insertion Administrator User User vectors Resource vectors wave vector Matching resources users Wave definition Resources Management Recommendations Users Management Waves Management wave vector Fig. 5. Flow of the recommender system for the wave definition. J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516 1511
1512 J. Serrano-Guerrero et al Information Sciences 181 (2011)1503-1516 Users in the system =。c Selected Users g. 6. Insertion of a new user. if they want to participate in the wave or not scoring the wave by means of a 2-tuple linguistic value bx E S2. The process is with all the system users a An analogous process is carried out for the resources. Each resource j of the system represented by its vector VR is com- and decide if the resource is inserted or not. If the resource is finally inserted the administrator has to score its importance for the wave using a 2-tuple linguistic value bx ES 3.4. 2. Insertion of a user ystem allows the user to express his preferences by means of 2-tuple linguistic labels instead of numerical values 3 When a new user is inserted, the system recommends to him the waves which are more interesting for his research lines. The user i fills out the registration form and a vector VU, is automatically generated and compared with the vectors(VU with j=(1,., m)) of the different m users registered in the system. The system compares the user vector VU with the vector VU of each registered userj(for all users)through the linguistic similarity measure o VUi, VU). If a VU, VU) exceeds a threshold y determined by the user(alvI, vU)>n)then the system onsiders that an affinity exists between VU and vU and therefore the waves of the user j are useful for the user i. This com- parison is repeated for each user j registered in the system collecting all their waves to be recommended to the new user i. The user i receives a list of waves interesting for his interests by email. This user i can accept or reject the subscription to each wave. If the invitation is accepted then it is necessary to complete the process by scoring the accepted wave with a 2- tuple linguistic value bx E S2 Taking advantage of that value bx e Sz that is stored for each registered user in the system, the information about each wave is submitted to the user i with a score that is the result of aggregating the scores of the most similar users. The aggre- gation is computed through Definition 3. Finally the user decides if the wave is relevant to him, and if the answer is affir native then the system informs the wave administrator about the presence of this user. hen the administrator alloy the insertion of the researcher, he assigns a new 2-tuple linguistic value bx E S2 or maintains the proposed one by the pre- vious aggregation and the insertion process is finished. The process is summarized in Fig. 6. 3.4.3. Insertion of a resource The process for a new resource is similar to the previously explained process for inserting a new user. The administrator of the wave inserts a new resource or receives a request for the insertion of a new resource by filling out a form with the main categories defined in ScienceDirect by means of linguistic labels. The user who suggests a resource assigns a 2-tuple nguistic value bx E S3 which indicates the possible importance of the resource for the wav When the new resource i is characterized by a user, the vector VR that represents that resource is compared with the m vectors of the resources stored in the system(VR;, withj=[1,., m)). The comparison is computed by the similarity measure aVR, VR). If the result exceeds the threshold i fixed by the wave administrator/s(oVR VR)>i)then the system considers that the resource j is related to the new resource i and consequently is useful for the same waves. When all resources have been compared with the new one, the waves of the resources which exceed the threshold i are collected and the new resource submitted to the wave administrator/s via email. The new resource is sent to the adminis- rators with an importance value which is the result of the aggregation of the importance degree bx E S3 assigned by each user of each resource VR from the wave that exceeds the threshold i in the comp puted using Definition 3. Finally the wave administrator decides if the resource is lL the aggregation is com- to the wave and if the an- swer is affirmative then the resource is scored with a 2-tuple linguistic value bx E S3, the result of the aggregation or a new value assigned by the administrator, and is available in the wave for the researchers
if they want to participate in the wave or not scoring the wave by means of a 2-tuple linguistic value bx 2 S2. The process is repeated with all the system users. An analogous process is carried out for the resources. Each resource j of the system represented by its vector VRj is compared with the wave vector VWi, and if rl(VWi,VRj) > k then the wave administrators receive information about that resource and decide if the resource is inserted or not. If the resource is finally inserted the administrator has to score its importance for the wave using a 2-tuple linguistic value bx 2 S3. 3.4.2. Insertion of a user Every user has to be registered into the system filling a registration form which contains the personal data and the user preferences with respect to the different disciplines discussed in Section 3.2. In order to facilitate the insertion process, the system allows the user to express his preferences by means of 2-tuple linguistic labels instead of numerical values. When a new user is inserted, the system recommends to him the waves which are more interesting for his research lines. The user i fills out the registration form and a vector VUi is automatically generated and compared with the vectors (VUj, with j = {1, ...,m}) of the different m users registered in the system. The system compares the user vector VUi with the vector VUj of each registered user j (for all users) through the linguistic similarity measure rl(VUi,VUj). If rl(VUi,VUj) exceeds a threshold c determined by the user (rl(VUi,VUj) > c) then the system considers that an affinity exists between VUi and VUj, and therefore the waves of the user j are useful for the user i. This comparison is repeated for each user j registered in the system collecting all their waves to be recommended to the new user i. The user i receives a list of waves interesting for his interests by email. This user i can accept or reject the subscription to each wave. If the invitation is accepted then it is necessary to complete the process by scoring the accepted wave with a 2- tuple linguistic value bx 2 S2. Taking advantage of that value bx 2 S2 that is stored for each registered user in the system, the information about each wave is submitted to the user i with a score that is the result of aggregating the scores of the most similar users. The aggregation is computed through Definition 3. Finally the user decides if the wave is relevant to him, and if the answer is affirmative then the system informs the wave administrator about the presence of this user. When the administrator allows the insertion of the researcher, he assigns a new 2-tuple linguistic value bx 2 S2 or maintains the proposed one by the previous aggregation and the insertion process is finished. The process is summarized in Fig. 6. 3.4.3. Insertion of a resource The process for a new resource is similar to the previously explained process for inserting a new user. The administrator of the wave inserts a new resource or receives a request for the insertion of a new resource by filling out a form with the main categories defined in ScienceDirect by means of linguistic labels. The user who suggests a resource assigns a 2-tuple linguistic value bx 2 S3 which indicates the possible importance of the resource for the wave. When the new resource i is characterized by a user, the vector VRi that represents that resource is compared with the m vectors of the resources stored in the system (VRj, with j = {1,...,m}). The comparison is computed by the similarity measure rl(VRi,VRj). If the result exceeds the threshold k fixed by the wave administrator/s (rl(VRi,VRj) > k) then the system considers that the resource j is related to the new resource i and consequently is useful for the same waves. When all resources have been compared with the new one, the waves of the resources which exceed the threshold k are collected and the new resource submitted to the wave administrator/s via email. The new resource is sent to the administrators with an importance value which is the result of the aggregation of the importance degree bx 2 S3 assigned by each user of each resource VRi from the wave that exceeds the threshold ki in the comparison rl(VRi,VRj); the aggregation is computed using Definition 3. Finally the wave administrator decides if the resource is really relevant to the wave and if the answer is affirmative then the resource is scored with a 2-tuple linguistic value bx 2 S3, the result of the aggregation or a new value assigned by the administrator, and is available in the wave for the researchers. Fig. 6. Insertion of a new user. 1512 J. Serrano-Guerrero et al. / Information Sciences 181 (2011) 1503–1516
