5174 C Porcel et al / Expert Systems with Applications 36(2009)5173-5183 research groups) TTO Fig 1. Main mission in a TTo the way in which it is possible to filter the great amount of infor- The recommender systems can be characterized because they mation available Recommender Systems are tools whose objective ( Hanani et al, 2001; Reisnick Varian, 1997) is to evaluate and filter the great amount of information available in a specific scope to assist the users in their information access are applicable for unstructured or semi-structured data (e.g. processes(Basu, Hirsh, Cohen, 1998: Cao Li, 2007: Hanani Web documents or e-mail messages). Shapira, Shoval, 2001: Hsu, 2008: Ungar, Pennock, Lawrence, the users have long time information needs that are described 2001: Reisnick varian, 1997). by means of user profiles, Another problem is the great variety of representations and handle large amounts of data, evaluations of the information. The problem becomes more notice-. deal primarily with textual data and able when users take part in the S.Therefore, to improve the their objective is to remove irrelevant data from incoming information representations and the user interface we need more ns of data items flexibility in the information processing. To solve this probler we propose the use of Fuzzy Linguistic Modeling(FLm)(Ben-Arieh Traditionally, recommender systems have fallen into two main Zhifeng, 2006: Herrera Herrera-Viedma, 1997: Herrera, categories(Good et al., 1999: Hanani et al Popescul et al, Herrera-Viedma, Martinez, 2008: Herrera, Herrera-Viedma, 2001: Reisnick Varian, 1997). Content- recommender Verdegay, 1996: Herrera Martinez, 2000: Zadeh, 1975)to repre- systems recommend the information by matching the terms used sent and handle flexible information by means of linguistic labels. in the representation of user profiles with the index terms used In this paper, we propose SIREZIN, a recommender system for in the representation of documents, ignoring data from other recommending research resources based on FLM. The system al- users. These recommender systems tend to fail when little lows the researchers to obtain automatically information about re- is known about user information needs. Collaborative recom search resources in their interest areas and it recommends about mender systems use explicit or implicit preferences from many panies or another researchers which could collaborate with users to recommend documents to a given user, ignoring the rep- them in projects(Chang, Wang. Wang, 2007; Chen& Ben-Arieh, resentation of documents. These recommender systems tend to 2006: Herrera MartInez, 2001: Herrera-Viedma, Cordon, Luqu fail when little is known about a user, or when he/she has uncom- Lopez, Munoz, 2003: Herrera-Viedma, Martinez, Mata, mon interests(Popescul et al., 2001). In these kind of systems, th Chiclana, 2005). SIREZIN is designed using both recommendation users'information preferences can be used to define user profiles hniques and the multi-granular FLM to represent and handle that are applied as filters to streams of documents: the recom flexible information by means of linguistic labels. To prove the sys- mendations to a user are based on another users' recommend. tem functionality we have implemented a primary version and the tions with similar profiles. The construction of accurate profiles experimental results shows its useful and effectiveness. is a key task and the systems success will depend on a large The paper is structured as follows: Section 2 revises the recom- extent on the ability of the learned profiles to represent the users mendation approaches and the FLM. Section 3 presents the design preferences(Quiroga Mostafa, 2002). Moreover, we can use a of the system, analyzing its architecture, data structure and activ- hybrid approach to smooth out the disadvantages of each one of ty. Section 4 reports the system evaluatio them and to exploit their benefits(Basu et al., 1998: Claypool, esults. Finally, we point out some concluding remarks. Gokhale, Miranda, 1999: Good et al., 1999: Popescul et al 2. Preliminaries On the other hand, we should point out that the matching pro- cess is a main process in the activity of the recommender systems. 2.1. Recommender systems The two major approaches followed in the design and implementa tion of recommender systems to do the matching are the statistical Information gathering in Internet is a complex activity. Find the approach and the knowledge based approach( Hanani et al, 2001) formation, required for the users, on the Web is not a In our system, we have applied the statistical approach. This ap- simple task. This problem is more acute with the ever increasing proach represents the documents and the user profiles as weighted Ise of the Internet. For example, users who subscribe to internet vectors of index terms To filter the information the system imple- lists waste a great deal of time reading, viewing or deleting irrele- ments a statistical algorithm that computes the similarity of a vee vant e-mail messages. To improve the information access on the tor of terms that represents the data item being filtered to a user's Web the users need tools to filter the great amount of information profile. The most common algorithm used is the Correlation or the available across the Web. Recommender systems can provide Cosine measure between the user's profile and the document's vec information services by delivering the information to people who tor( Korfhage, 1997). need it. It is a research area that offers tools for discriminating be- The recommendation activity is followed by a relevance feed- ween relevant and irrelevant information by providing personal- back phase Relevance feedback is a cyclic process whereby the user ized assistance for continuous retrieval of information(Reisnick feeds back into the system decisions on the relevance of retrieved varian, 1997). documents and the system then uses these evaluations to auto-the way in which it is possible to filter the great amount of infor￾mation available. Recommender Systems are tools whose objective is to evaluate and filter the great amount of information available in a specific scope to assist the users in their information access processes (Basu, Hirsh, & Cohen, 1998; Cao & Li, 2007; Hanani, Shapira, & Shoval, 2001; Hsu, 2008; Ungar, Pennock, & Lawrence, 2001; Reisnick & Varian, 1997). Another problem is the great variety of representations and evaluations of the information. The problem becomes more notice￾able when users take part in the process. Therefore, to improve the information representations and the user interface we need more flexibility in the information processing. To solve this problem we propose the use of Fuzzy Linguistic Modeling (FLM) (Ben-Arieh & Zhifeng, 2006; Herrera & Herrera-Viedma, 1997; Herrera, Herrera-Viedma, & Martı´ nez, 2008; Herrera, Herrera-Viedma, & Verdegay, 1996; Herrera & Martı´ nez, 2000; Zadeh, 1975) to repre￾sent and handle flexible information by means of linguistic labels. In this paper, we propose SIRE2IN, a recommender system for recommending research resources based on FLM. The system al￾lows the researchers to obtain automatically information about re￾search resources in their interest areas and it recommends about companies or another researchers which could collaborate with them in projects (Chang, Wang, & Wang, 2007; Chen & Ben-Arieh, 2006; Herrera & Martı´ nez, 2001; Herrera-Viedma, Cordón, Luque, López, & Muñoz, 2003; Herrera-Viedma, Martı´ nez, Mata, & Chiclana, 2005). SIRE2IN is designed using both recommendation techniques and the multi-granular FLM to represent and handle flexible information by means of linguistic labels. To prove the sys￾tem functionality we have implemented a primary version and the experimental results shows its useful and effectiveness. The paper is structured as follows: Section 2 revises the recom￾mendation approaches and the FLM. Section 3 presents the design of the system, analyzing its architecture, data structure and activ￾ity. Section 4 reports the system evaluation and the experimental results. Finally, we point out some concluding remarks. 2. Preliminaries 2.1. Recommender systems Information gathering in Internet is a complex activity. Find the appropriate information, required for the users, on the Web is not a simple task. This problem is more acute with the ever increasing use of the Internet. For example, users who subscribe to internet lists waste a great deal of time reading, viewing or deleting irrele￾vant e-mail messages. To improve the information access on the Web the users need tools to filter the great amount of information available across the Web. Recommender systems can provide information services by delivering the information to people who need it. It is a research area that offers tools for discriminating be￾tween relevant and irrelevant information by providing personal￾ized assistance for continuous retrieval of information (Reisnick & Varian, 1997). The recommender systems can be characterized because they (Hanani et al., 2001; Reisnick & Varian, 1997): are applicable for unstructured or semi-structured data (e.g. Web documents or e-mail messages), the users have long time information needs that are described by means of user profiles, handle large amounts of data, deal primarily with textual data and their objective is to remove irrelevant data from incoming streams of data items. Traditionally, recommender systems have fallen into two main categories (Good et al., 1999; Hanani et al., 2001; Popescul et al., 2001; Reisnick & Varian, 1997). Content-based recommender systems recommend the information by matching the terms used in the representation of user profiles with the index terms used in the representation of documents, ignoring data from other users. These recommender systems tend to fail when little is known about user information needs. Collaborative recom￾mender systems use explicit or implicit preferences from many users to recommend documents to a given user, ignoring the rep￾resentation of documents. These recommender systems tend to fail when little is known about a user, or when he/she has uncom￾mon interests (Popescul et al., 2001). In these kind of systems, the users’ information preferences can be used to define user profiles that are applied as filters to streams of documents; the recom￾mendations to a user are based on another users’ recommenda￾tions with similar profiles. 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). Moreover, 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, Gokhale, & Miranda, 1999; Good et al., 1999; Popescul et al., 2001). On the other hand, we should point out that the matching pro￾cess is a main process in the activity of the recommender systems. The two major approaches followed in the design and implementa￾tion of recommender systems to do the matching are the statistical approach and the knowledge based approach (Hanani et al., 2001). In our system, we have applied the statistical approach. This ap￾proach represents the documents and the user profiles as weighted vectors of index terms. To filter the information the system imple￾ments a statistical algorithm that computes the similarity of a vec￾tor of terms that represents the data item being filtered to a user’s profile. The most common algorithm used is the Correlation or the Cosine measure between the user’s profile and the document’s vec￾tor (Korfhage, 1997). 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￾Researchers (research groups) Environment companies TTO Generation of knowledge and its transfer to the society Fig. 1. Main mission in a TTO. 5174 C. Porcel et al. / Expert Systems with Applications 36 (2009) 5173–5183