Data sparsity is one of the biggest problems faced by collaborative filtering used in recommender systems. Data imputation alleviates the data sparsity problem by inferring missing ratings and imputing them to the original rating matrix. In this paper, we identify the limitations of existing data imputation approaches and suggest three new claims that all data imputation approaches should follow to achieve high recommendation accuracy. Furthermore, we propose a deep-learning based approach to compute imputed values that satisfies all three claims. Based on our hypothesis that most pre-use preferences (e.g., impressions) on items lead to their post-use preferences (e.g., ratings), our approach tries to understand via deep learning how pre-use preferences lead to post-use preferences differently depending on the characteristics of users and items. Through extensive experiments on real-world datasets, we verify our three claims and hypothesis, and also demonstrate that our approach significantly outperforms existing state-of-the-art approaches.
|Title of host publication||The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|Publication status||Published - 2018 Apr 10|
|Event||27th International World Wide Web, WWW 2018 - Lyon, France|
Duration: 2018 Apr 23 → 2018 Apr 27
|Name||The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018|
|Conference||27th International World Wide Web, WWW 2018|
|Period||18/4/23 → 18/4/27|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT: Ministry of Science and ICT) (No. NRF-2017R1A2B3004581). Also, this work was supported by the Naver Corporation, where Jung-Tae Lee and Jaeho Choi gave us good comments in a practical point of view, which helped us greatly in performing this research successfully.
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications