Abstract
With the spread of the Web, users can obtain a wide variety of information, and also can access novel content in real time. In this environment, finding useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. Techniques used for association rules in deriving user profiles are exploited for discovering useful and meaningful patterns of users. Each user preference is presented the frequent term patterns, collectively called PTP (Personalized Term Pattern) and the preference terms, called PT (Personalized Term). In addition, a content-based filtering approach is employed to recommend content corresponding with user preferences. In order to evaluate the performance of the proposed method, we compare experimental results with those of a probabilistic learning model and vector space model. The experimental evaluation on NSF research award datasets demonstrates that the proposed method brings significant advantages in terms of improving the recommendation quality in comparison with the other methods.
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References
Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24, 513–523 (1988)
Agrawal, R., Srikan, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int. Conf. on Very Large Data Bases (1994)
Chen, L., Sycara, K.: WebMate: Personal Agent for Browsing and Searching. In: Proc. of the 2nd Int. Conf. on Autonomous Agents and Multi Agent Systems, pp. 132–139 (1998)
Billsus, D., Pazzani, M.J.: A hybrid user model for News story classification. In: Proc. of the 7th Int. Conf. on User Modeling, pp. 99–108 (1999)
Schwab, I., Pohl, W., Koychev, I.: Learning to Recommend from Positive Evidence. In: Proc. of Int. Conf. on Intelligent User Interfaces (2000)
Widyantoro, D.H., Ioerger, T., Yen, J.: Learning User Interest Dynamics with a Three-Descriptor Representation. Journal of the American Society for Information Science and Technology 52, 212–225 (2001)
Aggarwal, C.C., Philip, S.Y.: An Automated System for Web Portal Personalization. In: Proc. of the 28th VLDB Conference, pp. 1031–1040 (2002)
Chen, C.C., Chen, M.C., Sun, Y.: PVA: A Self-Adaptive Personal View Agent. Journal of Intelligent Information Systems 18, 173–194 (2002)
Eirinaki, M., Vazirgiannis, M.: Web Mining for Web Personalization. ACM Transactions on Internet Technology 3, 1–27 (2003)
Deshpande, M., Karypis, G.: Item-based Top-N Recommendation Algorithms. ACM Transac-tions on Information Systems 22, 143–177 (2004)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)
Gabrilovich, E., Dumais, S., Horvitz, E.: Newsjunkie: Providing Personalized News-feeds via Analysis of Information Novelty. In: Proc. of the 13th Int. Conf. on World Wide Web, pp. 482–490 (2004)
Chung, S., McLeod, D.: Dynamic Pattern Mining: An Incremental Data Clustering Approach. In: Spaccapietra, S., Bertino, E., Jajodia, S., King, R., McLeod, D., Orlowska, M.E., Strous, L. (eds.) Journal on Data Semantics II. LNCS, vol. 3360, pp. 85–112. Springer, Heidelberg (2005)
Pazzani, M.J., Meyers, A.: NSF Research Awards Abstracts (1990-2003), http://kdd.ics.uci.edu/databases/nsfabs/nsfawards.html
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
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Kim, HN., Ha, I., Jung, JG., Jo, GS. (2007). User Preference Modeling from Positive Contents for Personalized Recommendation. In: Corruble, V., Takeda, M., Suzuki, E. (eds) Discovery Science. DS 2007. Lecture Notes in Computer Science(), vol 4755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75488-6_12
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DOI: https://doi.org/10.1007/978-3-540-75488-6_12
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