Version-Aware Rating Prediction for Mobile App Recommendation
Abstract
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 version-based 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 23, 2017
- Source ID
- 10.1145/3015458
Entities
People
- Feng Xu
- Hanghang Tong
- Jian Lu
- Wayne Xin Zhao
- Yaojing Wang
- Yuan Yao
Organizations
- Arizona State University
- Army Research Office
- Beijing Municipal Natural Science Foundation
- Defense Threat Reduction Agency
- Nanjing University
- National Institutes of Health
- National Natural Science Foundation of China
- Renmin University of China