Structural Analysis of User Choices for Mobile App Recommendation

TL;DR

This paper introduces a Structural User Choice Model (SUCM) that leverages app hierarchies and competition among apps to improve personalized mobile app recommendations, demonstrating significant performance gains over existing methods.

Contribution

The paper proposes a novel SUCM that captures hierarchical and competitive app relationships, along with an efficient learning algorithm, advancing personalized recommendation techniques.

Findings

SUCM outperforms state-of-the-art methods in experiments

Hierarchical and competitive app structures improve recommendation accuracy

Large-scale experiments validate the effectiveness of SUCM

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. While 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 paper, 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 data set collected from Google Play. The results show that SUCM consistently outperforms state-of-the-art top-N recommendation methods by a significant margin.