Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
The decision-theoretic interactive video advisor
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Identifying representative ratings for a new item in recommendation system
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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A recommender system utilizes in general an information filtering technique called collaborative filtering. To improve prediction quality, collaborative filtering needs reinforcements such as utilizing useful attributes of the items as well as a more refined neighbor selection. In this paper we present that the recommender systems that utilizing the attributes of the items in collaborative filtering improves prediction quality. The experimental results show that the recommender systems using the attributes provide better prediction qualities than other methods that do not utilize the attributes.