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research-article

Version-sensitive mobile App recommendation

Authors:

Tat-Seng ChuaAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 381, Issue C

Pages 161 - 175

https://doi.org/10.1016/j.ins.2016.11.025

Published: 01 March 2017 Publication History

Abstract

Being part and parcel of the daily life for billions of people all over the globe, the domain of mobile Applications (Apps) is the fastest growing sector of mobile market today. Users, however, are frequently overwhelmed by the vast number of released Apps and frequently updated versions. Towards this end, we propose a novel version-sensitive mobile App recommendation framework. It is able to recommend appropriate Apps to right users by jointly exploring the version progression and dual-heterogeneous data. It is helpful for alleviating the data sparsity problem caused by version division. As a byproduct, it can be utilized to solve the in-matrix and out-of-matrix cold-start problems. Considering the progression of versions within the same categories, the performance of our proposed framework can be further improved. It is worth emphasizing that our proposed version progression modeling can work as a plug-in component to be embedded into most of the existing latent factor-based algorithms. To support the online learning, we design an incremental update strategy for the framework to adapt the dynamic data in real-time. Extensive experiments on a real-world dataset have demonstrated the promising performance of our proposed approach with both offline and online protocols. Relevant data, code, and parameter settings are available at http://apprec.wixsite.com/version.

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Cited By

Zeng FLi YXiao JYang D(2024)DDHCNExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121564237:PBOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121564
Zhu KXiao YZheng WJiao XSun CHsu C(2021)Incorporating contextual information into personalized mobile applications recommendationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05988-825:16(10629-10645)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00500-021-05988-8
Jelodar HWang YXiao GRabbani MZhao RAyobi SHu PMasood I(2021)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05397-325:5(3675-3696)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s00500-020-05397-3
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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 381, Issue C

March 2017

371 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 March 2017

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Cited By

Zeng FLi YXiao JYang D(2024)DDHCNExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121564237:PBOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121564
Zhu KXiao YZheng WJiao XSun CHsu C(2021)Incorporating contextual information into personalized mobile applications recommendationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05988-825:16(10629-10645)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s00500-021-05988-8
Jelodar HWang YXiao GRabbani MZhao RAyobi SHu PMasood I(2021)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05397-325:5(3675-3696)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s00500-020-05397-3
Zhang MZhao JDong HDeng KLiu Y(2020)A Knowledge Graph Based Approach for Mobile Application RecommendationService-Oriented Computing10.1007/978-3-030-65310-1_25(355-369)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-65310-1_25
Gao SLiu LLiu YLiu HWang Y(2020)Updating the goal model with user reviews for the evolution of an appJournal of Software: Evolution and Process10.1002/smr.225732:8Online publication date: 27-Feb-2020
https://dl.acm.org/doi/10.1002/smr.2257
He JFang XLiu HLi X(2019)Mobile app recommendationMIS Quarterly10.25300/MISQ/2019/1504943:3(827-850)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.25300/MISQ/2019/15049
Liu XXie MWen XChen RGe YDuffield NWang N(2019)Micro- and macro-level churn analysis of large-scale mobile gamesKnowledge and Information Systems10.1007/s10115-019-01394-762:4(1465-1496)Online publication date: 21-Aug-2019
https://dl.acm.org/doi/10.1007/s10115-019-01394-7
Cao DHe XMiao LAn YYang CHong RCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Attentive Group RecommendationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3209998(645-654)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3209998
Xie FChen LYe YLiu YZheng ZLin X(2018)A Weighted Meta-graph Based Approach for Mobile Application Recommendation on Heterogeneous Information NetworksService-Oriented Computing10.1007/978-3-030-03596-9_29(404-420)Online publication date: 12-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-03596-9_29
Cao DNie LHe XWei XZhu SChua TKando NSakai TJoho HLi Hde Vries AWhite R(2017)Embedding Factorization Models for Jointly Recommending Items and User Generated ListsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080779(585-594)Online publication date: 7-Aug-2017
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