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 representationpeople to communicate and work together in new and more effective ways. The system is based on the Google Wave Fed￾eration 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 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) embedda￾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 within the platform are shared and therefore the users can correct information, append new information, comment the exist￾ing 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￾versation. 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 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) 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 [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 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 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