期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 10, 页码 4268-4280出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2900159
关键词
Feature extraction; Optimization; Sparse matrices; Motion pictures; Recommender systems; Estimation; Social networking (online); Content based; optimization; recommendation; relation learning; user preference
类别
资金
- National Key Research and Development Program of China [2018YFB1003800, 2018YFB1003805]
- National Natural Science Foundation of China [61572156, 61832004]
- Shenzhen Science and Technology Program [JCYJ20170413105929681, JCYJ20170811161545863]
Recommender systems are currently utilized widely in e-commerce for product recommendations and within content delivery platforms. Previous studies usually use independent features to represent item content. As a result, the relationship hidden among the content features is overlooked. In fact, the reason that an item attracts a user may be attributed to only a few set of features. In addition, these features are often semantically coupled. In this paper, we present an optimization model for extracting the relationship hidden in content features by considering user preferences. The learned feature relationship matrix is then applied to address the cold-start recommendations and content-based recommendations. It could also easily be employed for the visualization of feature relation graphs. Our proposed method was examined on three public datasets: 1) hetrec-movielens-2k-v2; 2) book-crossing; and 3) Netflix. The experimental results demonstrated the effectiveness of our method in comparison to the state-of-the-art recommendation methods.
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