How to impute missing ratings? Claims, solution, and its application to collaborative filtering

Youngnam Lee, Sang Wook Kim, Sunju Park, Xing Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages783-792
Number of pages10
ISBN (Electronic)9781450356398
DOIs
Publication statusPublished - 2018 Apr 10
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: 2018 Apr 232018 Apr 27

Publication series

NameThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
Country/TerritoryFrance
CityLyon
Period18/4/2318/4/27

Bibliographical note

Publisher Copyright:
© 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
  • Software

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