ABSTRACT
Different efforts have been made to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests. Web Content Recommendation has been an active application area for Information Filtering, Web Mining and Machine Learning research. Recent studies show that combining the conceptual and usage information can improve the quality of web recommendations. In this paper we exploit this idea to enhance a reinforcement learning framework, primarily devised for web recommendations based on web usage data. A hybrid web recommendation method is proposed by making use of the conceptual relationships among web resources to derive a novel model of the problem, enriched with semantic knowledge about the usage behavior. With our hybrid model for the web page recommendation problem we show the apt and flexibility of the reinforcement learning framework in the web recommendation domain, and demonstrate how it can be extended in order to incorporate various sources of information. We evaluate our method under different settings and show how this method can improve the overall quality of web recommendations.
- Bose, A., Beemanapalli, K., Srivastava, J., Sahar, S. Incorporating concept hierarchies into usage mining based recommendations. Proc. 8th WEBKDD workshop, 2006. Google ScholarDigital Library
- Burke, R. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 2002. Google ScholarDigital Library
- Chi, E. H., Pirolli, P., Pitkow, J. Using Information Scent to Model User Information Needs and Actions on the Web. Proceedings of Human Factors in Computing Systems, 2001. Google ScholarDigital Library
- Deshpande, M., Karypis, G. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems (TOIS), 2004. Google ScholarDigital Library
- Eirinaki, M., Vazirgiannis, M., Varlamis, I. SEWeP: Using Site Semantics and a Taxonomy to Enhance the Web Personalization Process, in Proc. of the 9th SIGKDD Conf. 2003. Google ScholarDigital Library
- Eirinaki, M., Lampos, C., Paulakis, S., Vazirgiannis, M. Web Personalization Integrating Content Semantics and Navigational Patterns. In Proceedings of the sixth ACM workshop on Web Information and Data Management WIDM 2004. Google ScholarDigital Library
- Fu, X., Budzik, J., Hammond, K. J. Mining navigation history for recommendation. Intelligent User Interfaces, 2000. Google ScholarDigital Library
- Godoy, D., Amandi, A. Modeling user interests by conceptual clustering. Special Issue on the Semantic Web and Web Services, Information Systems Journal, 2005.Google Scholar
- Li, J., Zaiane, O. R. Combining Usage, Content and Structure Data to Improve Web Site Recommendation, 5th International Conference on Electronic Commerce and Web, 2004Google Scholar
- Mobasher, B., Cooley, R., Srivastava, J. Automatic Personalization based on Web Usage Mining. Communications of the ACM. 43 (8), pp. 142--151, 2000. Google ScholarDigital Library
- Mobasher, B., Dai, H., Luo, T., Sun, Y., Zhu, J. Integrating web usage and content mining for more effective personalization. In EC-Web, pages 165--176, 2000. Google ScholarDigital Library
- Nakagawa M., Mobasher, B. A Hybrid Web Personalization Model Based on Site Connectivity. Proc. 5th WEBKDD workshop, 2003.Google Scholar
- Resnick, P., Varian, H. R. Recommender Systems. Communications of the ACM, 40 (3), 56--58, 1997. Google ScholarDigital Library
- Srivastava, J., Cooley, R., Deshpande, M., Tan, P. N. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, 1(2):12--23, 2000. Google ScholarDigital Library
- Sutton, R. S., Barto, A. G. Reinforcement Learning: An Introduction, MIT Press, Cambridge, 1998 Google ScholarDigital Library
- Taghipour, N., Kardan, A., Shiry Ghidary, S. Usage-Based Web Recommendations: A Reinforcement Learning Approach. Proceedings of the 1<sup>st</sup> ACM Recommender Systems Conference. 2007. Google ScholarDigital Library
Index Terms
- A hybrid web recommender system based on Q-learning
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