Improving Performance of Movie Recommendation in Collaborative Filtering Systems
نویسنده
چکیده
Collaborative filtering has been most widely used in commercial sites to recommend items based on the history of user preferences for items. The idea behind this method is to find similar users whose ratings for items are incorporated to make recommendation. Hence, similarity calculation is most critical in recommendation performance. For movie recommendation, this paper enhances performance of previous similarity measures by incorporating information on genre difference as well as the number of movies co-rated by two users. Extensive experiment results are provided to demonstrate far enhanced performance improvement for several popular classic CF methods.
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