research-article

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Version-Aware Rating Prediction for Mobile App Recommendation

Authors:

Wayne Xin Zhao,

Jian LuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 35, Issue 4

Article No.: 38, Pages 1 - 33

https://doi.org/10.1145/3015458

Published: 23 June 2017 Publication History

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.

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Zheng QYu DWang DYan WFu Y(2024)App Recommendation Model Based on Heterogeneous Graph Collaborative Filtering2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650932(1-7)Online publication date: 30-Jun-2024
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Wang XZhang TTan YShang WLi Y(2024)How to effectively mine app reviews concerning software ecosystem? A survey of review characteristicsJournal of Systems and Software10.1016/j.jss.2024.112040213(112040)Online publication date: Jul-2024
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Farshidi SRezaee KMazaheri SRahimi ADadashzadeh AZiabakhsh MEskandari SJansen S(2024)Understanding user intent modeling for conversational recommender systems: a systematic literature reviewUser Modeling and User-Adapted Interaction10.1007/s11257-024-09398-xOnline publication date: 6-Jun-2024
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Index Terms

Version-Aware Rating Prediction for Mobile App Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 35, Issue 4

Special issue: Search, Mining and their Applications on Mobile Devices

October 2017

461 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3112649

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

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Copyright © 2017 ACM.

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Publication History

Published: 23 June 2017

Accepted: 01 November 2016

Revised: 01 September 2016

Received: 01 June 2016

Published in TOIS Volume 35, Issue 4

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National Key Research and Development Program of China
Beijing Natural Science Foundation
Baidu gift
National 863 Program of China
Program A for Outstanding PhD candidate of Nanjing University
Army Research Office
DTRA
National Institutes of Health
National Natural Science Foundation of China
Region II University Transportation Center
Collaborative Innovation Center of Novel Software Technology and Industrialization

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https://doi.org/10.1109/IJCNN60899.2024.10650932
Wang XZhang TTan YShang WLi Y(2024)How to effectively mine app reviews concerning software ecosystem? A survey of review characteristicsJournal of Systems and Software10.1016/j.jss.2024.112040213(112040)Online publication date: Jul-2024
https://doi.org/10.1016/j.jss.2024.112040
Farshidi SRezaee KMazaheri SRahimi ADadashzadeh AZiabakhsh MEskandari SJansen S(2024)Understanding user intent modeling for conversational recommender systems: a systematic literature reviewUser Modeling and User-Adapted Interaction10.1007/s11257-024-09398-xOnline publication date: 6-Jun-2024
https://doi.org/10.1007/s11257-024-09398-x
Yu SZeng YPan YYang F(2023)SNMCF: A Scalable Non-Negative Matrix Co-Factorization for Student Cognitive ModelingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332873036:7(3486-3500)Online publication date: 30-Oct-2023
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Wang JLi TGao H(2023)Application Recommendation based on Metagraphs: Combining Behavioral and Published Information2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00039(250-259)Online publication date: Jun-2023
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Xie FZheng AChen LZheng ZTang M(2023)Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networksConnection Science10.1080/09540091.2023.228983436:1Online publication date: 30-Dec-2023
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Wang HLian DTong HLiu QHuang ZChen E(2021)HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware RecommendationACM Transactions on Information Systems10.1145/346391340:2(1-28)Online publication date: 27-Sep-2021
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