[Seminar] How to Impute Missing Ratings?: Claims, Solution, and Its Application
호스트: 강 유 교수(x7254)
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.
Sang-Wook Kim received the B.S. degree in Computer Engineering from Seoul National University, Korea at 1989, and earned the M.S. and Ph.D. degrees in Computer Science from Korea Advanced Institute of Science and Technology (KAIST), Korea at 1991 and 1994, respectively. From 1995 to 2003, he served as an Associate Professor of the Division of Computer, Information, and Communications Engineering at Kangwon National University, Korea. In 2003, he joined Hanyang University, Seoul, Korea, where he currently is a Professor at the Department of Computer Science & Engineering and the director of the Brain-Korea-21-Plus research program. He received the Presidential Award of Korea in 2017 for his academic achievement. He is an associate editor of Information Sciences. His research interests include databases, data mining, recommendation, and social network & media analysis.