research-article

New and improved: modeling versions to improve app recommendation

Authors:

Kazunari Sugiyama,

Tat-Seng ChuaAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 647 - 656

https://doi.org/10.1145/2600428.2609560

Published: 03 July 2014 Publication History

Abstract

Existing recommender systems usually model items as static -- unchanging in attributes, description, and features. However, in domains such as mobile apps, a version update may provide substantial changes to an app as updates, reflected by an increment in its version number, may attract a consumer's interest for a previously unappealing version. Version descriptions constitute an important recommendation evidence source as well as a basis for understanding the rationale for a recommendation. We present a novel framework that incorporates features distilled from version descriptions into app recommendation. We use a semi-supervised topic model to construct a representation of an app's version as a set of latent topics from version metadata and textual descriptions. We then discriminate the topics based on genre information and weight them on a per-user basis to generate a version-sensitive ranked list of apps for a target user. Incorporating our version features with state-of-the-art individual and hybrid recommendation techniques significantly improves recommendation quality. An important advantage of our method is that it targets particular versions of apps, allowing previously disfavored apps to be recommended when user-relevant features are added.

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https://doi.org/10.1007/s11227-024-06502-1
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https://doi.org/10.1109/GCAT59970.2023.10353414
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Index Terms

New and improved: modeling versions to improve app recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 03 July 2014

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SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.1007/s11227-024-06502-1
Azam FGloire MPriyadarshi NKumari S(2023)Text Classification into Emotional States Using Deep Learning based BERT Technique2023 4th IEEE Global Conference for Advancement in Technology (GCAT)10.1109/GCAT59970.2023.10353414(1-5)Online publication date: 6-Oct-2023
https://doi.org/10.1109/GCAT59970.2023.10353414
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
https://doi.org/10.1109/COMPSAC57700.2023.00039
Tafesse W(2023)The differential effects of developers’ app store strategy on the performance of niche and popular mobile appsJournal of Marketing Analytics10.1057/s41270-023-00216-811:3(317-330)Online publication date: 14-Mar-2023
https://doi.org/10.1057/s41270-023-00216-8
Motger QFranch XMarco J(2023)Mobile Feature-Oriented Knowledge Base Generation Using Knowledge GraphsNew Trends in Database and Information Systems10.1007/978-3-031-42941-5_24(269-279)Online publication date: 31-Aug-2023
https://doi.org/10.1007/978-3-031-42941-5_24
Liu XBao KKang LSun JShi Y(2023)The Dynamic Update of Mobile Apps: A Research Design with HMM MethodHCI in Business, Government and Organizations10.1007/978-3-031-35969-9_18(260-270)Online publication date: 17-Jul-2023
https://doi.org/10.1007/978-3-031-35969-9_18
Paula BCoelho JMano DCoutinho COliveira JRibeiro RBatista F(2022)Collaborative Filtering for Mobile Application Recommendation with Implicit Feedback2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference10.1109/ICE/ITMC-IAMOT55089.2022.10033307(1-9)Online publication date: 19-Jun-2022
https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033307
Li TXia TWang HTu ZTarkoma SHan ZHui P(2022)Smartphone App Usage Analysis: Datasets, Methods, and ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.316317624:2(937-966)Online publication date: Oct-2023
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Bonomo M(2022)Knowledge Extraction from Biological and Social GraphsNew Trends in Database and Information Systems10.1007/978-3-031-15743-1_60(648-656)Online publication date: 29-Aug-2022
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Rehman Khan HLim CAhmed MTan KBin Mokhtar M(2021)Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-TourismSustainability10.3390/su1315814113:15(8141)Online publication date: 21-Jul-2021
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