C Porcel et aL /Expert Systems with Applications 36(2009)12520-12528 12521 that reveals their areas of interest and as new materials are added A variety of techniques have been proposed as the basis for to the collection, they are compared to the profiles and relevant ommender systems. We can distinguish four different classes of items are sent to the users(Marchionini, 2009). ecommendation techniques based on the source of knowledge One interesting extension of this concept is to use the connec -(Burke, 2007: Hanani et al., 2001; Reisnick Varian, 1997): tivity inherent in digital libraries to support collaborative filtering. here users rate or add value to information objects and these rat Content-based systems: They generate the recommendations ings are shared with a large community, so that popular items can king into account the terms used in the items representation be easily located or people can search for objects found useful by and the ratings that a user has given to them(Basu, Hirsh, others with similar profiles(hanani, Shapira, Shoval, 2001: Mar- Cohen, 1998: Claypool, Gokhale, Miranda, 1999). These rec- hionini, 2009: Reisnick Varian, 1997). ommender systems tend to fail when little is known about the Digital libraries have been applied in many contexts but in this ser information needs paper we focus on an academic environment. University Digital Li Collaborative systems: The system generates recommendations braries(UDls) provide information resources and services to stu- using explicit or implicit preferences from many users, ignor ents, faculty and staff in an environment that supports learning. items representation. Collab teaching and research( Chao, 2002). peer users with a rating history similar to the current user In this paper we propose a fuzzy linguistic recommender sys- and they generate recommendations using this neighborhood tem to achieve major advances in the activities of UDL in order (Good et aL, 1999: Renda& Straccia, 2005 ). to improve their performance. The system is oriented to research Demographic systems: A demographic recommender system ers and it recommends two types of resources: in the first place. provides recommendations based on a demographic profile specialized resources of the user research area, and in the second of the user. Recommended items can be generated for differ place, complementary resources in order to include resources of ent demographic niches, by combining the ratings of users related areas that could be interesting to discover collaboration in those niches(Pazzani, 1999) possibilities with other researchers and to form multi-disciplina Knowledge-based systems: These systems generate the recom- groups. As in( Porcel, Lopez-Herrera, Herrera-Viedma, 2009) we endations based on the inferences about items that satisfy combine a recommender system, to filter out the information, with the users from the information provided by each user regard a multi-granular Fuzzy Linguistic Modeling(FLm), to represent and ing his her knowledge about items that can be recommended handle flexible information by means of linguistic labels(Chang Wang, Wang, 2007; Chen& Ben-Arieh, 2006: Herrera Martinez 2001: Herrera-Viedma, Cordon, Luque, Lopez, Munoz, 2003: All these techniques have benefits and disadvantages. However, Herrera-Viedma, Martinez, Mata, Chiclana, 2005: Herrera, we can use a hybrid approach to smooth out the disadvantages of Herrera-Viedma, Martinez, 2008). each one of them and to exploit their benefits(basu et al, 1998 The paper is structured as follows. Section 2 revises some pre- Claypool et al, 1999: Good et al., 1999). In these kind of systems. iminaries, i.e., the concept and main aspects about recommender the users' information preferences can be used to define user pro- systems and the approaches of FLM that we use to the system de- files that are applied as filters to streams of documents. Therefore, sign, the 2-tuple FLM and the multi-granular FLM. In Section 3 we the construction of accurate profiles is a key task and the system present a multi-disciplinar fuzzy linguistic recommender systems success will depend on a large extent on the ability of the learned to advice research resources in UDL Section 4 reports the system profiles to represent the user's preferences (Quiroga Mostafa evaluation and some experimental results. Finally, some conclud- 2002). narks are pointed out. The recommendation activity is followed by a relevance feed back phase Relevance feedback is a cyclic process whereby the user feeds back into the system decisions on the relevance of retrieved 2. Preliminaries documents and the system then uses these evaluations to auto- matically update the user profile( Hanani et al, 2001; Reisnick 2.1. Recommender systems Recommender systems could be defined as systems that pro- 2. 2. Fuzzy linguistic modeling ndividualized recommendations as output or has the effect hiding the user in a personalized way to interesting or useful The use of fuzzy sets theory has given very good results for cts in a large space of possible options(Burke 2002). They modeling qualitative information(Zadeh, 1975)and it has pro- becoming popular tools for reducing information overload ven to be useful in many problems, e.g., in decision making and for improving the sales in nerce web sites(Burke,(Cabrerizo, Alonso, Herrera-Viedma, 2009: Herrera, Herrera 2007: Cao Li, 2007: Hsu, 2008: Reisnick Varian, 1997) Viedma, Verdegay, 1996: Mata, Martinez, Herrera-Viedma It is a research area that offers tools for discriminating between 2009), quality evaluation(Herrera-Viedma, Pasi, Lopez-Herrera relevant and irrelevant information by providing personalized Porcel, 2006: Herrera-Viedma Peis, 2003 ). models of ssistance for continuous information accesses, filtering the infor- information retrieval(Herrera-Viedma, 2001 a, 2001 b: Herrera mation and delivering it to people who need it(Reisnick Varian, Viedma Lopez-Herrera, 2007: Herrera-Viedma, Lopez-Herrera 997). Automatic filtering services differ from retrieval services in Luque, Porcel, 2007: Herrera-Viedma, Lopez-Herrera, Porcel that in filtering the corpus changes continuously, the users have 2005), and political analysis(Arfi, 2005). It is a tool based on long time information needs( described by mean of user profiles the concept of linguistic variable proposed by Zadeh(1975 ). Next instead of to introduce a query into the system )and their objective we analyze the two approaches of FLM that we use in is to remove irrelevant data from incoming streams of data items system (Hanani et al., 2001; Marchionini, 2009: Reisnick varian, 1997). A result from a recommender system is understood com- 2. 2.1. The 2-tuple fuzzy linguistic mendation, an option worthy of consideration; a result from an The 2-tuple FLM(Herrera 2,2000)isa information retrieval system is interpreted as a match to the users of which allows query(Burke, 2007). he loss of information typical of other fuzzy linguistithat reveals their areas of interest and as new materials are added to the collection, they are compared to the profiles and relevant items are sent to the users (Marchionini, 2009). One interesting extension of this concept is to use the connec￾tivity inherent in digital libraries to support collaborative filtering, where users rate or add value to information objects and these rat￾ings are shared with a large community, so that popular items can be easily located or people can search for objects found useful by others with similar profiles (Hanani, Shapira, & Shoval, 2001; Mar￾chionini, 2009; Reisnick & Varian, 1997). Digital libraries have been applied in many contexts but in this paper we focus on an academic environment. University Digital Li￾braries (UDLs) provide information resources and services to stu￾dents, faculty and staff in an environment that supports learning, teaching and research (Chao, 2002). In this paper we propose a fuzzy linguistic recommender sys￾tem to achieve major advances in the activities of UDL in order to improve their performance. The system is oriented to research￾ers and it recommends two types of resources: in the first place, specialized resources of the user research area, and in the second place, complementary resources in order to include resources of related areas that could be interesting to discover collaboration possibilities with other researchers and to form multi-disciplinar groups. As in (Porcel, López-Herrera, & Herrera-Viedma, 2009) we combine a recommender system, to filter out the information, with a multi-granular Fuzzy Linguistic Modeling (FLM), to represent and handle flexible information by means of linguistic labels (Chang, Wang, & Wang, 2007; Chen & Ben-Arieh, 2006; Herrera & Martínez, 2001; Herrera-Viedma, Cordón, Luque, López, & Muñoz, 2003; Herrera-Viedma, Martínez, Mata, & Chiclana, 2005; Herrera, Herrera-Viedma, & Martínez, 2008). The paper is structured as follows. Section 2 revises some pre￾liminaries, i.e., the concept and main aspects about recommender systems and the approaches of FLM that we use to the system de￾sign, the 2-tuple FLM and the multi-granular FLM. In Section 3 we present a multi-disciplinar fuzzy linguistic recommender systems to advice research resources in UDL. Section 4 reports the system evaluation and some experimental results. Finally, some conclud￾ing remarks are pointed out. 2. Preliminaries 2.1. Recommender systems Recommender systems could be defined as systems that pro￾duce individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options (Burke, 2002). They are becoming popular tools for reducing information overload and for improving the sales in e-commerce web sites (Burke, 2007; Cao & Li, 2007; Hsu, 2008; Reisnick & Varian, 1997). It is a research area that offers tools for discriminating between relevant and irrelevant information by providing personalized assistance for continuous information accesses, filtering the infor￾mation and delivering it to people who need it (Reisnick & Varian, 1997). Automatic filtering services differ from retrieval services in that in filtering the corpus changes continuously, the users have long time information needs (described by mean of user profiles instead of to introduce a query into the system) and their objective is to remove irrelevant data from incoming streams of data items (Hanani et al., 2001; Marchionini, 2009; Reisnick & Varian, 1997). A result from a recommender system is understood as a recom￾mendation, an option worthy of consideration; a result from an information retrieval system is interpreted as a match to the user’s query (Burke, 2007). A variety of techniques have been proposed as the basis for rec￾ommender systems. We can distinguish four different classes of recommendation techniques based on the source of knowledge (Burke, 2007; Hanani et al., 2001; Reisnick & Varian, 1997): Content-based systems: They generate the recommendations taking into account the terms used in the items representation and the ratings that a user has given to them (Basu, Hirsh, & Cohen, 1998; Claypool, Gokhale, & Miranda, 1999). These rec￾ommender systems tend to fail when little is known about the user information needs. Collaborative systems: The system generates recommendations using explicit or implicit preferences from many users, ignor￾ing the items representation. Collaborative systems locate peer users with a rating history similar to the current user and they generate recommendations using this neighborhood (Good et al., 1999; Renda & Straccia, 2005). Demographic systems: A demographic recommender system provides recommendations based on a demographic profile of the user. Recommended items can be generated for differ￾ent demographic niches, by combining the ratings of users in those niches (Pazzani, 1999). Knowledge-based systems: These systems generate the recom￾mendations based on the inferences about items that satisfy the users from the information provided by each user regard￾ing his/her knowledge about items that can be recommended (Burke, 2002). All these techniques have benefits and disadvantages. However, we can use a hybrid approach to smooth out the disadvantages of each one of them and to exploit their benefits (Basu et al., 1998; Claypool et al., 1999; Good et al., 1999). In these kind of systems, the users’ information preferences can be used to define user pro- files that are applied as filters to streams of documents. Therefore, the construction of accurate profiles is a key task and the system’s success will depend on a large extent on the ability of the learned profiles to represent the user’s preferences (Quiroga & Mostafa, 2002). The recommendation activity is followed by a relevance feed￾back phase. Relevance feedback is a cyclic process whereby the user feeds back into the system decisions on the relevance of retrieved documents and the system then uses these evaluations to auto￾matically update the user profile (Hanani et al., 2001; Reisnick & Varian, 1997). 2.2. Fuzzy linguistic modeling The use of fuzzy sets theory has given very good results for modeling qualitative information (Zadeh, 1975) and it has pro￾ven to be useful in many problems, e.g., in decision making (Cabrerizo, Alonso, & Herrera-Viedma, 2009; Herrera, Herrera￾Viedma, & Verdegay, 1996; Mata, Martínez, & Herrera-Viedma, 2009), quality evaluation (Herrera-Viedma, Pasi, López-Herrera, & Porcel, 2006; Herrera-Viedma & Peis, 2003), models of information retrieval (Herrera-Viedma, 2001a, 2001b; Herrera￾Viedma & López-Herrera, 2007; Herrera-Viedma, López-Herrera, Luque, & Porcel, 2007; Herrera-Viedma, López-Herrera, & Porcel, 2005), and political analysis (Arfi, 2005). It is a tool based on the concept of linguistic variable proposed by Zadeh (1975). Next we analyze the two approaches of FLM that we use in our system. 2.2.1. The 2-tuple fuzzy linguistic approach The 2-tuple FLM (Herrera & Martínez, 2000) is a continuous model of representation of information which allows to reduce the loss of information typical of other fuzzy linguistic approaches C. Porcel et al. / Expert Systems with Applications 36 (2009) 12520–12528 12521