Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (9): 160-167.doi: 10.11896/jsjkx.220700035

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-task Graph-embedding Deep Prediction Model for Mobile App Rating Recommendation

LI Haiming1, ZHU Zhiheng1, LIU Lei2, GUO Chenkai3   

  1. 1 College of Computer Science and Technology,Shanghai University of Electric Power,Shanghai 201306,China
    2 College of Artificial Intelligence,Nankai University,Tianjin 300350,China
    3 College of Cyber Science,Nankai University,Tianjin 300350,China
  • Received:2022-07-04 Revised:2022-10-12 Online:2023-09-15 Published:2023-09-01
  • About author:LI Haiming,born in 1964,Ph.D,professor,master supervisor.His main research interests include intelligent information processing and power informatization.
    GUO Chenkai,born in 1988,Ph.D,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include intelligent software engineering and code analysis of mobile app.
  • Supported by:
    National Natural Science Foundation of China(62002177),Science and Technology Planning Project of Tianjin City(20YDTPJC01810),2022 Experimental Course Reform Project of Nankai University(22NKSYSX05) and Ministry of Education Industry University Cooperation Collaborative Education Project(202002142035).

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Abstract: With the prevalence of smart terminal devices and mobile application(app for short),the requirements for application quality and user experience gradually increase.As an effective pre-assessment method,mobile app rating recommendation has gained increasing attention from app markets.The traditional app rating and recommendation works mainly focus on challenges such as data sparsity and model depth.Nevertheless,they fail to accurately capture the graph relationship within the apps and users.Furthermore,the multi-task characteristic of app recommendation is neglected.Aiming at these shortcomings,this paper proposes a graph embedding multi-task model AppGRec for mobile app rating and recommendation.AppGRec uses the embedding structure of inductive bipartite graph to mine the user interaction features.It uses the shared-bottom based model to capture the multi-task feature in app rating,while considering the effects of data sparsity and model depth.16 031 valid mobile apps and their feature data on Google Play are collected as dataset for method evaluation.Experimental results show that AppGRec achieves 10.4% and 10.9% improvement in terms of MAE and RMSE respectively comparing with the state-of-the-art models.In addition,this paper also makes quantitative analysis of the impact of hyperparameters and some core modules in AppGRec,and verifies the effectiveness from multiple perspectives.

Key words: Mobile app, Recommendation system, Graph embedding, Deep learning, Neural network, Rating prediction

CLC Number: 

  • TP391
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