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  1. Proceedings of the 1st International Workshop on Crowd-based Software Development Methods and Technologies (CrowdSoft 2014)
  2. Personalized mobile application discovery
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How the crowd impacts commercial applications: a user-oriented approach
Recommending relevant projects via user behaviour: an exploratory study on github
SmartHR: a resume query and management system based on semantic web
Crowdsourcing in the Brazilian IT industry: what we know and what we don't know
Exploring the patterns of social behavior in GitHub
Personalized mobile application discovery
Using clustering and transitivity to reduce the costs of crowdsourced entity resolution
Investigating social media in GitHub's pull-requests: a case study on Ruby on Rails
A novel multilayered context awareness technology for internetware evolution
iTest: testing software with mobile crowdsourcing
Estimating the dynamic performance of composed services: a probability theory based approach

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Personalized mobile application discovery

Content Provider ACM Digital Library
Author Yang, Cheng Wang, Tao Wang, Huaimin Yin, Gang Wu, Ming Xiao, Ming
Abstract With the dramatic growing of mobile application markets, users can find apps with any function they desire in these markets. However, the huge amounts of apps make it quite a challenge for users to discover good applications efficiently. Previous studies recommend applications based on the download history, user ratings or app usage records. Most of these studies fail to capture users' personal interests in mobile applications precisely. In this paper, we leverage apps as features for describing user's personal interests and propose a novel approach to do personalized recommendation. We introduce a Small-Crowd model to distinguish apps at reflecting users' personal interests, and design a weighting method to rank the installed apps for users by combining the global download information with fine-grained app usage records. The extensive experiments validate the effectiveness of our approach which outperforms state-of-the-art method. .
Starting Page 49
Ending Page 54
Page Count 6
File Format PDF
ISBN 9781450332248
DOI 10.1145/2666539.2666574
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-17
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Personalized recommendation Mobile app Small-crowd
Content Type Text
Resource Type Article
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