C Porcel et aL /Expert Systems with Applications 36(2009)12520-12528 Precision■ Recall OF1 9000 8000 700 6000 50.00 4000 30.00 2000 Users Fig. 5. Experimental result. 5 Conclusions Cao, Y.& Li, Y.(2007). An intelligent fuzzy-base Chang, S. L, Wang. R C,& Wang, S.Y(2007) Applying a direct multigranularity Internet access has resulted in digital libraries that are increas- nguistic and strategy-oriented aggregation approach on the assessment of ingly used by diverse communities for diverse pur and in 1015 performance. European Journal of operational Research, 177(2), ments. Users of UDL need tools to assist them in their processes of cha development as testig f enterit ibr marionsSaie te eserc 24 information gathering because of the large amount of information available on these systems. We have presented a multi-disciplinar Chen, Z& Ben-Anieh, D(2006). On the fusion of multi-granularity linguistic lab fuzzy linguistic recommender system to spread research resources in UDL The proposed system is oriented to researchers who re- Claypool, M. Gokhale, A,& Miranda, T(1999). Combining content-based and ive recommendations about resources that could be interesting for them. In particular, it is a hybrid recommender system that Cleverdon. c ow. men, E. M(1966)D e TnwaMmehn E y" Proj. incorporates complementary recommendations. The system filters the incoming information stream to spread the information to the fitting users, and when new users are inserted into the system, A, Fox, E A, Watson, L T,& Kipp, N. A (2004)Streams, structures S: A formal model for digital libraries. ACM ey receive interesting information for them to improve the ser vices that a UDL provides, it additionally recommends complemen- Good, N- Schafer, JB, Konstan J.A 1. Herlocker, J. L, et aL tary resources that allow researchers to discover collaboration possibilities with other colleagues and to form multi-disciplinar conference ce(pp.439-44 groups. The multi-granular fuzzy linguistic modeling has been ap- Hanani, U, Shapira, B.& Shoval, P(2001). Information filterit plied in order to improve the users-system interaction and the interpretability of the system activities. The experimental results Herrera, F.& Herrera-Viedma, E.(1997). Aggregatio weighted information. IEEE Transactions on Systems, Man and Cybernetics, Part A: how great user satisfaction with the received recommendations ethodology to deal with unbalanced linguistic term sets. IEEE Transactions cknowledgements Fuzzy Systems, 16(2), 354-370. n group decision making using linguistic OwA operators. Fuzzy Sets and This paper has been ed with the financing of SAINFO WEB Project(TIC00602 DER funds in FUZZYLING Project Herrera, F, Martinez, L(2000).A 2-tuple fuzzy linguistic representation model for (TN2007-61079), PETRI (PET2007-0460). and Project of computing with words. IEEE Transactions on Fuzzy Systems, 8(6). 740 Ministry of Public Works(90/07). Herrera, F.& Martinez, L(2001) A model based on linguistic 2-tuples for dealing References aking. IEEE Transactions on Systems, Man and Cybernetics. Part B: Cybernetics. 31(2)227-234. Herrera-Viedma, E.(2001a) g the retrieval process of an information Arf, B(2005). Fuzzy decision making in politics. A linguistic fuzzy-set approach Basu, C. Hirsh, H,& Cohen, w.(1998) Recom ation as classification: Us Herrera-Viedma, E (2001b). An information retrieval system with ordinal linguistic and content-based information in recommendation. In Proc. of th weighted queries based on two weighting elements. International Joumal of pp.714-720) Bordogna, G,& Pasi, G(1993) A fuzzy linguistic approach generalizing boolean Herrera-Viedma, E, Cordon, o M, Lopez, A G.& Munoz, A M.(2003). A information retrieval: A model and its evaluation. Journal of the American Society on Science. 44. 70-82. der systems. User Modeling and User-Adapted Herrera-Viedma, E,& L6pez-Herrera, A G(2007). A model of information retrieval 12(4).331-370 stem with unbalanced fuzzy linguistic information. International Journal of Burke, R(2007) Hybrid web recommender systems. In P Brusilovsky, A Kobsa, w. Herrera-viedma, E, Lopez-Herrera, A G ,& Porcel, meaton, A Beaulieu, M, Borlund, P, Brusilovsky, P Chalmers, M, et al WorkingGroupReportAvailablefrom<http://www.di2.nsf.gov/Herrera-viedma,E,MartinezL,Mata,F.,&chiclana internationalprojects/working-group_reports/personalisation. html>. system model for group decision-making5. Conclusions Internet access has resulted in digital libraries that are increas￾ingly used by diverse communities for diverse purposes, and in which sharing and collaboration have become important social ele￾ments. Users of UDL need tools to assist them in their processes of information gathering because of the large amount of information available on these systems. We have presented a multi-disciplinar fuzzy linguistic recommender system to spread research resources in UDL. The proposed system is oriented to researchers who re￾ceive recommendations about resources that could be interesting for them. In particular, it is a hybrid recommender system that incorporates complementary recommendations. The system filters the incoming information stream to spread the information to the fitting users, and when new users are inserted into the system, they receive interesting information for them. To improve the ser￾vices that a UDL provides, it additionally recommends complemen￾tary resources that allow researchers to discover collaboration possibilities with other colleagues and to form multi-disciplinar groups. The multi-granular fuzzy linguistic modeling has been ap￾plied in order to improve the users-system interaction and the interpretability of the system activities. The experimental results show great user satisfaction with the received recommendations. Acknowledgements This paper has been developed with the financing of SAINFO￾WEB Project (TIC00602) and FEDER funds in FUZZYLING Project (TIN2007-61079), PETRI project (PET2007-0460), and Project of Ministry of Public Works (90/07). 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. 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