Abstract
This paper describes the possible use of advanced content-based recommendation methods in the area of Digital Libraries. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to recommend new interesting documents. One of the main limitations of traditional keyword-based approaches is that they are unable to capture the semantics of the user interests, due to the natural language ambiguity. We developed a semantic recommender system, called ITem Recommender, able to disambiguate documents before using them to learn the user profile. The Conference Participant Advisor service relies on the profiles learned by ITem Recommender to build a personalized conference program, in which relevant talks are highlighted according to the participant’s interests.
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References
Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using wordnet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)
Callan, J.P., Smeaton, A.F.: Personalization and recommender systems in digital libraries joint nsf-eu delos working group report. Technical report (2003)
Degemmis, M., Lops, P., Basile, P.: An intelligent personalized service for conference participants. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 707–712. Springer, Heidelberg (2006)
Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits wordnet-based user profiles for neighborhood formation. User Modeling and User-Adapted Interaction, 217–255 (2007)
Guarino, N., Masolo, C., Vetere, G.: Content-based access to the web. IEEE Intelligent Systems 14(3), 70–80 (1999)
Magnini, B., Strapparava, C.: Improving user modelling with content-based techniques. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS (LNAI), vol. 2109, pp. 74–83. Springer, Heidelberg (2001)
Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. In: chapter 7: Word Sense Disambiguation, pp. 229–264. The MIT Press, Cambridge, US (1999)
Miller, G.: Wordnet: An on-line lexical database (Special Issue). International Journal of Lexicography 3(4) (1990)
Mladenic, D.: Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems 14(4), 44–54 (1999)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proc. of the 5th ACM Conference on Digital Libraries, San Antonio, US, pp. 195–204. ACM Press, New York (2000)
Pazzani, M., Billsus, D.: Learning and revising user profiles: The identification of interesting web sites. Machine Learning 27(3), 313–331 (1997)
Resnik, P.: Disambiguating noun groupings with respect to WordNet senses. In: Proceedings of the Third Workshop on Very Large Corpora, pp. 54–68. Association for Computational Linguistics (1995)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1) (2002)
Semeraro, G., Degemmis, M., Lops, P., Basile, P.: Combining Learning and Word Sense Disambiguation for Intelligent User Profiling. In: IJCAI. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, January 6-12, 2007, pp. 2856–2861. Morgan Kaufmann, San Francisco (2007)
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Semeraro, G., Basile, P., de Gemmis, M., Lops, P. (2007). Content-Based Recommendation Services for Personalized Digital Libraries. In: Thanos, C., Borri, F., Candela, L. (eds) Digital Libraries: Research and Development. DELOS 2007. Lecture Notes in Computer Science, vol 4877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77088-6_8
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DOI: https://doi.org/10.1007/978-3-540-77088-6_8
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