Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization
نویسندگان
چکیده
منابع مشابه
Learning Bidirectional Similarity for Collaborative Filtering
Memory-based collaborative filtering aims at predicting the utility of a certain item for a particular user based on the previous ratings from similar users and similar items. Previous studies in finding similar users and items are based on user-defined similarity metrics such as Pearson Correlation Coefficient or Vector Space Similarity which are not adaptive and optimized for different applic...
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Matrix factorization (MF) has been demonstrated to be one of the most competitive techniques for collaborative filtering. However, state-of-the-art MFs do not consider contextual information, where ratings can be generated under different environments. For example, users select items under various situations, such as happy mood vs. sad, mobile vs. stationary, movies vs. book, etc. Under differe...
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ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2011
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-011-0211-4