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An improved recommender system to avoid the persistent information overload in a university digital library

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Nowadays we are continuously bombarded with a lot of information, and because of it we have serious problems with accessing the relevant information, that is, we suffer from the information overload problems. Recommender systems have been applied successfully to avoid the information overload in different domains, but the number of electronic resources daily generated keeps growing and the problem rises again. Therefore, we find a persistent problem of information overload. In this paper we propose an improved recommender system to avoid the persistent information overload found in a University Digital Library. The idea is to include a memory to remember selected resources but not recommended to the user, and in such a way, the system could incorporate them in future recommendations to complete the set of filtered resources, for example, if there are a few resources to be recommended or if the user wishes output obtained by combination of resources selected in different recommendation rounds.
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  • ALONSO, S., CHICLANA, F., HERRERA, F., HERRERA-VIEDMA, E., ALCALA-FDEZ, J. and PORCEL C. (2008) A Consistency-Based Procedure to Estimating Missing Pairwise Preference Values. International Journal of Intelligent Systems 23, 155-175.
  • ALONSO, S., HERRERA-VIEDMA, E., CHICLANA, F. and HERRERA, F. (2009) Individual and Social Strategies to Deal With Ignorance Situations in Multi-Person Decision Making. International Journal of Information Technology and Decision Making 8 (2), 313-333.
  • BOBADILLA, J., SERRADILLA, F. and HERNANDO, A. (2009) Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems 22 (4), 261-265.
  • BOBADILLA, J., SERRADILLA, F. and BERNAL, J. (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Systems 23 (6), 520-528.
  • BURKE, R. (2007) Hybrid Web Recommender Systems. In: P. Brusilovsky, A. Kobsa and W. Nejdl, eds., The Adaptive Web, LNCS 4321, 377-408.
  • BUTCHER, H. (1998) Meeting Managers’ Information Needs. A Managing In-formation Report. London, Aslib, The Association for Information Management.
  • CABRERIZO, F.J., ALONSO, S. and HERRERA-VIEDMA, E. (2009) A Consensus Model for Group Decision Making Problems with Unbalanced Fuzzy Linguistic Information. International Journal of Information Technology & Decision Making 8 (1), 109-131.
  • CALLAN, J. et al. (2003) Personalisation and Recommender Systems in Digital Libraries. Joint NSF-EU DEL OS Working Group Report.
  • CAO, Y. and 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. and WANG, S.Y. (2007) Applying a direct Multi-granularity linguistic and strategy-oriented aggregation approach on the assessment of supply performance. European Journal of Operational Research 177 (2), 1013-1025.
  • CHAO, H. (2002) Assessing the quality of academic libraries on the Web: The development and testing of criteria. Library & Information Science Re-search 24, 169-194.
  • CLEVERDON, C.W. and KEEN, E.M. (1966) Factors Determining the Performance of Indexing Systems, Vol. 2 - Test Results. ASLIB Cranfield Res. Proj., Cranfield, Bedford, England.
  • CORNELIS, C., LU, J., GUO, X. and ZHANG, G. (2007) One-and-only item recommendation with fuzzy logic techniques. Information Sciences 177 (22), 4906-4921.
  • DUEN-REN, L., CHIN-HUI, L. and WANG-JUNG, L. (2009) A hybrid of se-quential rules and collaborative filtering for product recommendation. Information Sciences 179, 3505-3519.
  • EDMUNDS, A. and MORRIS, A. (2000) The problem of information overload in business organizations: a review of the literature. International Journal of Information Management 20, 17-28.
  • GONÇALVES, M.A., Fox, E.A., WATSON, L.T. and KIPP, N.A. (2004) Streams, structures, spaces, scenarios, societies (5s): A formal model for digital libraries. ACM Trans, on Information Systems 22 (2), 270-312.
  • HANANI, U., SHAPIRA, B. and SHOVAL, P. (2001) Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction 11, 203-259.
  • HERRERA, F. and HERRERA-VIEDMA, E. (2000) Linguistic decision analysis: Steps for solving decisions problems under linguistic information. Fuzzy Sets and Systems 115, 67-82.
  • HERRERA, F., HERRERA-VIEDMA, E. and VERDEGAY, J.L. (1997) Linguistic Measures Based on Fuzzy Coincidence for Reaching Consensus in Group Decision Making. International Journal of Approximate Reasoning 16, 309-334
  • HERRERA, F. and MARTÍNEZ, L. (2000) A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems 8 (6), 746-752.
  • HERRERA, F. and MARTÍNEZ, L. (2001) A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multi-expert decision-making. IEEE Transactions on Systems, Man and Cybernetics. Part B: Cybernetics 31 (2), 227-234.
  • 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., ALONSO,S., CHICLANA, F. and HERRERA, F. (2007) A consensus model for group decision making with incomplete fuzzy preference relations. IEEE Transactions on Fuzzy Systems 15 (5), 863-877.
  • HERRERA-VIEDMA,E., CHICLANA, F., HERRERA, F. and ALONSO, S. (2007) Group decision making model with incomplete fuzzy preference relations based on additive consistency. IEEE Transactions on Systems, Man and Cybernetics, Part B 37 (1), 176-189.
  • HERRERA-VIEDMA, E., CORDÓN, O., LUQUE, M., LÓPEZ, A.G. and 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.
  • HERRERA-VIEDMA, E. and LÓPEZ-HERRERA, A.G. (2007) A model of infor-mation retrieval system with unbalanced fuzzy linguistic information. International Journal of Intelligent Systems 22 (11), 1197-1214.
  • HERRERA-VIEDMA, E., LÓPEZ-HERRERA, A.G., LUQUE, M. and PORCEL, C. (2007) A Fuzzy Linguistic IRS Model Based on a 2-Tuple Fuzzy Linguistic Approach. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 15 (2), 225-250.
  • HERRERA-VIEDMA, E., MARTÍNEZ, L., MATA, F. and CHICLANA, F. (2005) A Consensus Support System Model for Group Decision-making Problems with Multi-granular Linguistic Preference Relations. IEEE Transactions on Fuzzy Systems 13 (5), 644-658.
  • HERRERA-VIEDMA, E., PASI, G., LÓPEZ-HERRERA, A.G., and PORCEL, C. (2006) Evaluating the information quality of web sites: A methodology based on fuzzy computing with words. Journal of the American Society for Information Science and Technology 57 (4), 538-549.
  • HERRERA-VIEDMA, E. and PEIS, E. (2003) Evaluating the informative quality of documents in SGML-format using fuzzy linguistic techniques based on computing with words. Information Processing and Management 39 (2), 233-249.
  • ISKOLD, A. (2007) The Art, Science and Business of Recommendation Engines,
  • KORFHAGE, R.R. (1997) Information Storage and Retrieval. Wiley Computer Publishing, New York.
  • LEUNG, C.W., CHAN, S.C. and CHUNG, F. (2008) An empirical study of a cross-level association rule mining approach to cold-start recommendations. Knowledge-Based Systems, 21 (7), 515-529.
  • MARCHIONINI, G. (2000) Research and Development in Digital Libraries.
  • MARTÍNEZ, L., PÉREZ, L.G., BARRANCO, M. and ESPINILLA, M. (2008) Effectiveness of Knowledge Based Recommender Systems Using Incomplete Linguistic Preference Relations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16 (2), 33-56.
  • MATA, F., MARTÍNEZ, L. and HERRERA-VIEDMA, E. (2009) An Adaptive Consensus Support Model for Group Decision Making Problems in a Multi-Granular Fuzzy Linguistic Context. IEEE Transactions on Fuzzy Systems 17 (2), 279-290.
  • MEGHABGHAB, G. and KANDEL, A. (2008) Search Engines, Link Analysis, and Users Web Behavior. Springer-Verlag, Berlin Heidelberg.
  • MORALES DEL CASTILLO, J.M., PEDRAZA-JIMÉNEZ, R., RUÍZ, A.A., PEIS, E. and HERRERA-VlEDMA, E. (2009) A Semantic Model of Selective Dissemination of Information for Digital Libraries. Information Technology and Libraries 28(1), 22-31.
  • NELSON, M.R. (1994) We have the information you want, but getting it will cost you: being held hostage by information overload. Crossroads: Special issue on the Internet 1 (1), 11-15.
  • PORCEL, C., MORENO, J.M. and HERRERA-VIEDMA, E. (2009) Amulti-dis-ciplinar recommender system to advice research resources in University Digital Libraries. Expert Systems with Applications 36 (10), 12520-12528.
  • PORCEL, C. and HERRERA-VIEDMA, E. (2010) Dealing with incomplete in-formation in a fuzzy linguistic recommender system to disseminate information in university digital libraries. Knowledge-Based Systems 23 (1), 32-39.
  • QUIROGA, L.M. and MOSTAFA, J. (2002) An experiment in building profiles in information filtering: the role of context of user relevance feedback. Information Processing and Management 38, 671-694.
  • RENDA, M.E. and STRACCIA, U. (2005) A personalized collaborative Digital Library environment: a model and an application. Information Processing and Management 41, 5-21.
  • REISNICK, P. and VARIAN, H.R. (1997) Recommender Systems. Special issue of Communications of the ACM 40 (3), 56-59.
  • ROSS, L. and SENNYEY, P. (2008) The Library is Dead, Long Live the Library! The Practice of Academic Librarianship and the Digital Revolution. The Journal of Academic Librarianship 34 (2), 145-152.
  • SYMEONIDIS, P., NANOPOULOS, A., PAPADOPOULOS, A.N. and MANOLO-POULOS, Y. (2008) Collaborative recommender systems: Combining effectiveness and efficiency. Expert Systems with Applications 34, 2995-3013.
  • TORRA, V. and NARUKAWA, Y. (2007) Modeling Decisions: Information Fusion and Aggregation Operators. Springer.
  • YANG, J.M and LI, K.F. (2009) Recommendation based on rational inferences in collaborative filtering Knowledge-Based Systems 22 (1), 105-114.
  • ZADEH, L.A. (1975) The concept of a linguistic variable and its applications to approximate reasoning. Information Sciences. Part I. 8, 199-249. Part II. 8, 301-357. Part III. 9, 43-80.
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