C Porcel et al Expert Systems with Applications 36(2009)5173-5183 Experimental contingency table User User9 6 489 Table 4.3 Acknowledgement Detailed experiment result Recall(g) F1(x) This paper has been developed with the financing of SAINFO- 8.82 WEB Project(TICo0602) FUZZYLING Project (TIN2007-61079) 66.67 7059 and PETRI Project(PET2007-0460) User4 67 63.33 References 5.00 Arfi, B (2005). Fuzzy decision making in politics: A linguistic fuzzy-set approach (LFSA) Political Analysis, 13(1), 23-56. 75.00 14 Basu, C Hirsh, H, Cohen, W,(1998) Recommendation as 44 57.1 5000 gs of the 71.43 6250 fifteenth national conference ificial intelligence. (pp. 71 Average 51.1 6767 57.62 Ben-Arieh, D,& Zhifeng, C(2006). Linguistic labels aggreg ons. IEEE n Systems, Man, and Cybernetics Part A- Systems and Humans. nt fuzzy-based recommendation system for 202 Chang, S L Wang R C,& Wang S.Y(2007). Applying a direct multigranularity - Recall inguistic and strategy-oriented aggregation approach on the assessment of Chen, Z,& Ben-Arieh, D (2006) On the fusion of multi-granularity linguistic label ats in group decision making. Computers and Industrial Engineering. 51( 26-54 termining the performance of xing systems. Test results. ASLIB Cranfield research project (VoL. 2) Cordon, O, Herrera, F,& Zwir, L.(2001). Linguistic modelling by hierarchical 20.00 Good.n. sha (1999). Combining collaborative filtering with personal agents for better 0.00 recommendations. In Proceedings of the sixteenth national conference 23 56 7 Hanani, U, Shapira, B, Shoval, P(2001). Information filtering: Overview ofissues Users esearch and systems. User Modeling and User-Adapted Interaction, 11.203- weighted information. IEEE Transactions on Systems, Man and Cybernetics, Part A: Herrera, F, Herrera-Viedma, E,& Martinez, L(2008)A fuzzy linguistic 5. Concluding remarks nethodology to deal with unbalanced linguistic term sets. IEEE Transactions on Systems,16(2)354-3 The exponential increase of Web sites and documents is con- Herrera, F, Hemera v eaman verdegay s, Land e) A inguistigeciszon process tributing to that Internet users not being able to find the informa- Herrera, F, Herrera-Viedma, E, Verdegay. ].L(1996 tion they seek in a simple and timely manner. Because of this, users in group decision making using linguistic OWA operators. Fuzzy Sets and re in need of tools to assist them cope with the large amount of Herrera, F- Herrera-Viedma, E,& Verdegay, ] L(1998). Choice for non- information available on the Web and they receive by email. In this per, we have studied a particular case of information we have presented SiREZIN, a recommender system using both Herrera, F.& Martinez, L(2000). A 2-tuple fuzzy linguistic nformation filtering tools and FLM. The proposed system is ori ted to researchers of the University and environment companie d allows them to obtain automatically information about re- search resources interesting for them. In particular, it is a system tems, Man and Cybernetics. Part B: Cybernetics, 31(2)227-234. based on both content-based filtering tools and the multi-granular Heretaievied syas.( sing M odinag the y tnevas c acess ch anounmormot ithe the information to the fitting users and recommends them:间间 lities. The FLM has been applied in order to mprove the experts-system interaction and researchers-system teraction. Experimental results have shown the useful and effec tiveness of our systems Intemational Journal of Approximate5. Concluding remarks The exponential increase of Web sites and documents is con￾tributing to that Internet users not being able to find the informa￾tion they seek in a simple and timely manner. Because of this, users are in need of tools to assist them cope with the large amount of information available on the Web and they receive by email. In this paper, we have studied a particular case of information access and we have presented SIRE2IN, a recommender system using both information filtering tools and FLM. The proposed system is ori￾ented to researchers of the University and environment companies and allows them to obtain automatically information about re￾search resources interesting for them. In particular, it is a system based on both content-based filtering tools and the multi-granular FLM. The system filters the incoming information stream to spread the information to the fitting users and recommends them about collaboration possibilities. The FLM has been applied in order to improve the experts-system interaction and researchers-system interaction. Experimental results have shown the useful and effec￾tiveness of our systems. Acknowledgement This paper has been developed with the financing of SAINFO￾WEB Project (TIC00602), FUZZYLING Project (TIN2007-61079) and PETRI Project (PET2007-0460). References Arfi, B. (2005). Fuzzy decision making in politics: A linguistic fuzzy-set approach (LFSA). Political Analysis, 13(1), 23–56. Basu, C., Hirsh, H., & Cohen, W., (1998). Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the fifteenth national conference on artificial intelligence. (pp. 714–720). Ben-Arieh, D., & Zhifeng, C. (2006). Linguistic labels aggregation and consensus measure for autocratic decision-making using group recommendations. IEEE Transactions on Systems, Man, and Cybernetics Part A – Systems and Humans, 36(3), 558–568. Cao, Y., & Li, Y. (2007). An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Systems with Applications, 33, 230–240. Chang, S. L., Wang, R. C., & Wang, S. Y. (2007). Applying a direct multigranularity linguistic and strategy-oriented aggregation approach on the assessment of supply performance. European Journal of Operational Research, 177(2), 1013–1025. Chen, Z., & Ben-Arieh, D. (2006). On the fusion of multi-granularity linguistic label sets in group decision making. Computers and Industrial Engineering, 51(3), 526–541. Claypool, M., Gokhale, A., & Miranda, T., (1999). Combining content-based and collaborative filters in an online newpaper. In Proceedings of the ACM SIGIR workshop on recommender systems-implementation and evaluation. Cleverdon, C. W., & Keen, E. M. (1966). Factors determining the performance of indexing systems. Test results. ASLIB Cranfield research project (Vol. 2). Bedford, England: Cranfield. Cordón, O., Herrera, F., & Zwir, I. (2001). Linguistic modelling by hierarchical systems of linguistic rules. IEEE Transactions on Fuzzy Systems, 10(1), 2–20. Good, N., Shafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B. M., Herlocker, J. L., et al. (1999). Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the sixteenth national conference on artificial intelligence (pp. 439–446). Hanani, U., Shapira, B., & Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11, 203– 259. Herrera, F., & Herrera-Viedma, E. (1997). Aggregation operators for linguistic weighted information. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems, 27, 646–656. Herrera, F., Herrera-Viedma, E., & Martı´nez, L. (2008). A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Transactions on Fuzzy Systems, 16(2), 354–370. Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1996). A linguistic decision process in group decision making. Group Decision and Negotiation, 5, 165–176. Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1996). Direct approach processes in group decision making using linguistic OWA operators. Fuzzy Sets and Systems, 79, 175–190. Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1998). Choice processes for non￾homogeneous group decision making in linguistic setting. Fuzzy Sets and Systems, 94, 287–308. Herrera, F., & Martı´nez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746–752. Herrera, F., & Martı´nez, L. (2001). A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision￾making. IEEE Transactions on Systems, Man and Cybernetics. Part B: Cybernetics, 31(2), 227–234. Herrera-Viedma, E. (2001). Modeling the retrieval process of an information retrieval system using an ordinal fuzzy linguistic approach. Journal of the American Society for Information Science and Technology, 52(6), 460–475. Herrera-Viedma, E. (2001). An information retrieval system with ordinal linguistic weighted queries based on two weighting elements. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9, 77–88. Herrera-Viedma, E., Cordón, O., Luque, M., López, A. G., & Muñoz, A. M. (2003). A model of fuzzy linguistic IRS based on multi-granular linguistic information. International Journal of Approximate Reasoning, 34(3), 221–239. Table 4.2 Experimental contingency table User1 User2 User3 User4 User5 User6 User7 User8 User9 User10 Nrs5 6 3 4 5 6 5 3 4 5 Nrn3 2 2 2 3 2 2 1 3 2 Nis4 3 6 5 4 3 4 6 5 4 Nr8 8 5 6 8 8 7 4 7 7 Ns9 9 9 9 9 9 9 9 9 9 Table 4.3 Detailed experiment result Precision (%) Recall (%) F1 (%) User1 55.56 62.50 58.82 User2 66.67 75.00 70.59 User3 33.33 60.00 42.86 User4 44.44 66.67 53.33 User5 55.56 62.50 58.82 User6 66.67 75.00 70.59 User7 55.56 71.43 62.50 User8 33.33 75.00 46.15 User9 44.44 57.14 50.00 User10 55.56 71.43 62.50 Average 51.11 67.67 57.62 Fig. 8. Experiment result. 5182 C. Porcel et al. / Expert Systems with Applications 36 (2009) 5173–5183