Abstract
In this paper, we present a new approach that is a synergy of item-based Collaborative Filtering (CF) and Case Based Reasoning (CBR) for personalized recommendations. We present a two-phase strategy: in phase I, we developed a context-sensitive item-based CF method that leverages the original past recommendations of peers via ratings performed on various information items. In phase II, we further personalize the information items comprising multiple components using a CBR-based compositional adaptation technique to selectively collect the most relevant information components and combine them into one composite recommendation. In this way, our approach allows fine-grained information filtering by operating at the constituent elements of an information item as opposed to the entire information item. We show that our strategy improves the quality and relevancy of the recommendations in terms of its appropriateness to the user’s needs and interests, and validated by statistical significance tests. We demonstrate the working of our strategy by recommending personalized music playlists.
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Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. In: AI Communication. IOS Press, Amsterdam (1994)
Abidi, S.S.R.: Designing Adaptive Hypermedia for Internet Portals: A Personalization Strategy Featuring Case Based Reasoning with Compositional Adaptation. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527. Springer, Heidelberg (2002)
Belkin, N.J., Croft, W.B.: Information Filtering and Information Retrieval. Two Sides of the Same Coin. Communications of the ACM 35, 29–38 (1992)
Dilley, R.: The problem of Context. Berghahn Books, New York (1999)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp. 175–186 (1994)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the International WWW Conference (10), Hong Kong (2001)
Sarwar, B., Karypis, J., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: 2nd Conf. on Electronic Commerce (EC 2000), New York (2000)
Wilke, W., Bergmann, R.: Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving. In: Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (1998)
Cotter, P., Smyth, B.: PTV: Intelligent Personalized TV Guides. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence. AAAI Press, MIT Press, pp. 957–964 (2000)
Geong, Y. Y.: An Analysis of Collaborative Filtering Systems. KMS Research Paper, School of Information, University of Texas, Austin (2003)
Schmidt, R., Vorobieva, O., Gierl, L.: Case-Based Adaptation Problems in Medicine. In: Pro-ceedings of WM 2003: Professionelles Wissensmanagement, KollenVerlag Bonn (2003)
Reyhani, N., Badie, K., Kharrat, M.: A New Approach to Compositional Adaptation Based on Optimizing the Global Distance Function and Its Application in an Intelligent Tutoring System. In: Proceedings of 2003 IEEE Intl. Conf. on Information Reuse and Integration, Las Vegas, USA, pp. 285–290 (2003)
Daniel Wayne, W.: Biostatistics, A Foundation for Analysis in the Health Sciences. Wiley, Chichester (1987)
Kobsa, A.: Customized Hypermedia Presentation Techniques for Improving Online Customer Relationships. Knowledge Engineering Review 16(2), 111–155 (1999)
Abidi, S.S.R., Chong, Y., Abidi, S.R.: Patient Empowerment Via ‘Pushed’ Delivery of Customized Healthcare Educational Content Over the Internet. In: 10th World Congress on Medical Informatics, London (2001)
Henze, N., Nejdl, W.: Extensible Adaptive Hypermedia Courseware: Integrating Different Courses and Web Material. In: Brusilovsky, P., Stock, O., Strappavara, C. (eds.) Adaptive Hypermedia and Adaptive Web-based Systems, pp. 109–120. Springer, Heidelberg (2000)
Aguzzoli, S., Avesani, P., Masssa, P.: Compositional CBR via Collaborative Filtering. In: Proceedings of ICCBR 2001 Workshop on CBR in Electronic Commerce, Vancouver, Canada (2001)
Burke, R.: A Case-Based Approach to Collaborative Filtering. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898.Springer, Heidelberg (2000)
Winterfeld, D., von Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge (1986)
Goker, M.H.: Workshop on Case Based Reasoning and Personalization. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416. Springer, Heidelberg (2002)
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Chedrawy, Z., Abidi, S.S.R. (2006). An Adaptive Personalized Recommendation Strategy Featuring Context Sensitive Content Adaptation. In: Wade, V.P., Ashman, H., Smyth, B. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2006. Lecture Notes in Computer Science, vol 4018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_8
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DOI: https://doi.org/10.1007/11768012_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34696-8
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