Version-Aware rating prediction for mobile app recommendation

Yuan Yao, Wayne Xin Zhao, Yaojing Wang, Hanghang Tong, Feng Xu, Jian Lu

Research output: Contribution to journalArticlepeer-review


With the great popularity of mobile devices, the amount of mobile apps has grown at a more dramatic rate than ever expected. A technical challenge is how to recommend suitable apps to mobile users. In this work, we identify and focus on a unique characteristic that exists in mobile app recommendation-That is, an app usually corresponds to multiple release versions. Based on this characteristic, we propose a fine-grain version-Aware app recommendation problem. Instead of directly learning the users' preferences over the apps, we aim to infer the ratings of users on a specific version of an app. However, the user-version rating matrix will be sparser than the corresponding user-App rating matrix, making existing recommendation methods less effective. In view of this, our approach has made two major extensions. First, we leverage the review text that is associated with each rating record; more importantly, we consider two types of versionbased correlations. The first type is to capture the temporal correlations between multiple versions within the same app, and the second type of correlation is to capture the aggregation correlations between similar apps. Experimental results on a large dataset demonstrate the superiority of our approach over several competitive methods.

Original languageEnglish (US)
Article number3015458
JournalACM Transactions on Information Systems
Issue number4
StatePublished - Jun 2017
Externally publishedYes


  • App rating prediction
  • Recommender systems
  • Version correlation

ASJC Scopus subject areas

  • Information Systems
  • General Business, Management and Accounting
  • Computer Science Applications


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