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.
|Original language||English (US)|
|Title of host publication||The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||7|
|State||Published - May 13 2019|
|Event||2019 World Wide Web Conference, WWW 2019 - San Francisco, United States|
Duration: May 13 2019 → May 17 2019
|Name||The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019|
|Conference||2019 World Wide Web Conference, WWW 2019|
|Period||5/13/19 → 5/17/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
Copyright 2019 Elsevier B.V., All rights reserved.
- Collaborative filtering
- Matrix completion
- Matrix factorization
- Recommender systems