Mobile App Recommendation: An Involvement-Enhanced Approach

65 Pages Posted: 29 Nov 2018

See all articles by Jiangning He

Jiangning He

Shanghai University of Finance and Economics

Xiao Fang

Lerner College of Business and Economics, University of Delaware

Hongyan Liu

Tsinghua University - School of Economics & Management

Xindan Li

Nanjing University

Date Written: November 6, 2018

Abstract

Given the ubiquitous and critical role of mobile apps in people’s lives as well as the sheer size of the mobile app market, developing effective mobile app recommendation methods that can help users locate the mobile apps they desire is critical for both mobile app users and platforms. Premised in involvement theory, we propose a novel mobile app recommendation method that integrates both users’ app download behaviors and app browsing behaviors for mobile app recommendations, in contrast to existing methods that rely on download behaviors but neglect browsing behaviors. Specifically, we introduce a novel model that appropriately combines download and browsing behaviors to learn users’ overall interests in and involvement with apps, we develop a new algorithm to infer the model’s parameters, and we propose an innovative mobile app recommendation strategy that combines users’ overall interests and their current interests to recommend apps. Finally, using data collected from one of the largest mobile app platforms in China, we demonstrate and analyze the superior performance of our method over several state-of-the-art mobile app recommendation methods.

Keywords: mobile app recommendation, data mining, machine learning, graphical model, product involvement

Suggested Citation

He, Jiangning and Fang, Xiao and Liu, Hongyan and Li, Xindan, Mobile App Recommendation: An Involvement-Enhanced Approach (November 6, 2018). Available at SSRN: https://ssrn.com/abstract=3279195 or http://dx.doi.org/10.2139/ssrn.3279195

Jiangning He

Shanghai University of Finance and Economics ( email )

777 Guoding Road
Shanghai, AK Shanghai 200433
China

Xiao Fang (Contact Author)

Lerner College of Business and Economics, University of Delaware ( email )

Newark, DE 19716
United States

Hongyan Liu

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China

Xindan Li

Nanjing University ( email )

Nanjing, Jiangsu 210093
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
509
Abstract Views
2,124
rank
70,903
PlumX Metrics