ABSTRACT
We have analyzed logs that which web pages are viewed by users. Information recommendation is one of hot research areas for users activities support. Although many of recommendation systems are eager to match a user's preference, if the user does not want that at that moment, it would be just a noise no matter how much match the preference matches user's preference over all. It is important to understand what the user really wants each of moment timely. Therefore, in this paper, we make use of the following two characteristics for inference user's temporal wish. First is to adapt the degree of user's each interest with time range evolution. Second, web browsing logs related to an activity has been temporarily reinforced. The preliminary result of an algorithm is introduced.
- B.Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pp. 285--295. ACM, 2001. Google ScholarDigital Library
- G.Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, Vol.17, No.6, pp. 734--749, 2005. Google ScholarDigital Library
- B.N. Miller, J. A. Konstan, and J. Riedl. Pocketlens: Toward a personal recommender system. ACM Transactions on Information Systems (TOIS), Vol.22, No.3, pp. 437--476, 2004. Google ScholarDigital Library
- J.Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative filtering recommender systems. The Adaptive Web, pp. 291--324, 2007 Google ScholarDigital Library
Index Terms
- User profile generation reflecting user's temporal preference through web life-log
Recommendations
User preference representation based on psychometric models
ADC '11: Proceedings of the Twenty-Second Australasian Database Conference - Volume 115Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity ...
User preference through learning user profile for ubiquitous recommendation systems
KES'06: Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IAs ubiquitous commerce is coming, the ubiquitous recommendation systems utilize collaborative filtering to help users with fast searches for the best suitable items by analyzing the similar preference. However, collaborative filtering may not provide ...
Context-Aware Web Services Recommendation Based on User Preference
APSCC '14: Proceedings of the 2014 Asia-Pacific Services Computing ConferenceContext-Aware Recommender System aims to recommend items not only similar to those already rated with the highest score, but also that could combine the contextual information with the recommendation process. Existing context-aware Web services ...
Comments