Structural analysis of user choices for mobile app recommendation

Bin Liu, Yao Wu, Neil Zhenqiang Gong, Junjie Wu, Hui Xiong, Martin Ester

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Advances in smartphone technology have promoted the rapid development of mobile apps. However, the availability of a huge number of mobile apps in application stores has imposed the challenge of finding the right apps to meet the user needs. Indeed, there is a critical demand for personalized app recommendations. Along this line, there are opportunities and challenges posed by two unique characteristics of mobile apps. First, app markets have organized apps in a hierarchical taxonomy. Second, apps with similar functionalities are competing with each other. Although there are a variety of approaches for mobile app recommendations, these approaches do not have a focus on dealing with these opportunities and challenges. To this end, in this article, we provide a systematic study for addressing these challenges. Specifically, we develop a structural user choice model (SUCM) to learn fine-grained user preferences by exploiting the hierarchical taxonomy of apps as well as the competitive relationships among apps. Moreover, we design an efficient learning algorithm to estimate the parameters for the SUCM model. Finally, we perform extensive experiments on a large app adoption dataset collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art Top-N recommendation methods by a significant margin.

Original languageEnglish (US)
Article number17
JournalACM Transactions on Knowledge Discovery from Data
Volume11
Issue number2
DOIs
StatePublished - Nov 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Keywords

  • Hierarchy structure
  • Mobile apps
  • Recommender systems
  • Structural choices

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