Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2016

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.

Keywords

Mobile app markets, app tagging, online kernel learning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

WSDM '16: Proceedings of the 9th ACM International Conference on Web Search and Data Mining: February 22-25, San Franciso

First Page

63

Last Page

72

ISBN

9781450337168

Identifier

10.1145/2835776.2835812

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/2835776.2835812

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