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research-article

Mobile App Tagging

Authors:

Steven C.H. Hoi,

Xiaokui XiaoAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 63 - 72

https://doi.org/10.1145/2835776.2835812

Published: 08 February 2016 Publication History

Abstract

Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. Specifically, given a novel query app without tags, our proposed framework (i) first explores online kernel learning techniques to retrieve a set of top-N similar apps that are semantically most similar to the query app from a large app repository; and (ii) then mines the text data of both the query app and the top-N similar apps to discover the most relevant tags for annotating the query app. To evaluate the efficacy of our proposed framework, we conduct an extensive set of experiments on a large real-world dataset crawled from Google Play. The encouraging results demonstrate that our technique is effective and promising.

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Cited By

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
Kabaha ADrachsler-Cohen D(2022)Boosting Robustness Verification of Semantic Feature NeighborhoodsStatic Analysis10.1007/978-3-031-22308-2_14(299-324)Online publication date: 2-Dec-2022
https://doi.org/10.1007/978-3-031-22308-2_14
Maheswari MGeetha SKumar SKaruppiah MSamanta DPark Y(2021)PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile ApplicationsIEEE Access10.1109/ACCESS.2021.30535839(20819-20827)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3053583
Show More Cited By

Index Terms

Mobile App Tagging
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 February 2016

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Singapore Management University
MOE, Singapore

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WSDM 2016

Sponsor:

WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

Acceptance Rates

WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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
Kabaha ADrachsler-Cohen D(2022)Boosting Robustness Verification of Semantic Feature NeighborhoodsStatic Analysis10.1007/978-3-031-22308-2_14(299-324)Online publication date: 2-Dec-2022
https://doi.org/10.1007/978-3-031-22308-2_14
Maheswari MGeetha SKumar SKaruppiah MSamanta DPark Y(2021)PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile ApplicationsIEEE Access10.1109/ACCESS.2021.30535839(20819-20827)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3053583
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
Soares FRebechi RStevenson M(2020)SciBabel: a system for crowd-sourced validation of automatic translations of scientific textsGenomics & Informatics10.5808/GI.2020.18.2.e2118:2(e21)Online publication date: 30-Jun-2020
https://doi.org/10.5808/GI.2020.18.2.e21
Mao KXiao XZhu JLu BTang RHe XHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based ApproachProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401438(2327-2336)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401438
Tu ZLi YHui PSu LJin D(2020)Personalized Mobile App Recommendation by Learning User's Interest from Social MediaIEEE Transactions on Mobile Computing10.1109/TMC.2019.292938819:11(2670-2683)Online publication date: 1-Nov-2020
https://doi.org/10.1109/TMC.2019.2929388
Uddin MHe QHan JChua C(2020)Comparison of Text-Based and Feature-Based Semantic Similarity Between Android AppsWeb Information Systems Engineering – WISE 202010.1007/978-3-030-62005-9_38(530-545)Online publication date: 18-Oct-2020
https://doi.org/10.1007/978-3-030-62005-9_38
Chen XWang YHe JPan SLi YZhang P(2019)CAPProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33143913:1(1-25)Online publication date: 29-Mar-2019
https://dl.acm.org/doi/10.1145/3314391
Ochiai KPutri FFukazawa Y(2019)Local App Classification using Deep Neural Network based on Mobile App Market Data2019 IEEE International Conference on Pervasive Computing and Communications (PerCom10.1109/PERCOM.2019.8767416(186-191)Online publication date: Mar-2019
https://doi.org/10.1109/PERCOM.2019.8767416
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