期刊
WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)
卷 -, 期 -, 页码 3223-3229出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3308558.3313736
关键词
Recommender systems; Collaborative filtering; Matrix completion; Matrix factorization
资金
- NSF [1447788, 1704074, 1757916, 1834251]
- Army Research Office [W911NF1810344]
- Intel Corp
- Digital Technology Center at the University of Minnesota
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1447788] Funding Source: National Science Foundation
- Division Of Materials Research
- Direct For Mathematical & Physical Scien [1834251] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1704074, 1757916] Funding Source: National Science Foundation
- U.S. Department of Defense (DOD) [W911NF1810344] Funding Source: U.S. Department of Defense (DOD)
Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches. Also, we show that the number of ratings that an item or a user has positively correlates with the ability of low-rank matrix-completion-based approaches to predict the ratings for the item or the user accurately. Furthermore, we use these insights to develop four matrix completion-based approaches, i.e., Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization (TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional matrix-completion-based approaches for the users and the items with few ratings in the user-item rating matrix.
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