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	An Approach for Cross-Community Content Recommendation:A Case Study on Docker
	Yang Yong ; Li Ying ; Tang Hongyan ; Jia Tong ; Shao Wenlong
	2016
关键词	Cross-community LDA Recomendation Information aggregation
英文摘要	With the boom of open source software,open source communities are formed and involved in software development,deployment and application with unprecedented level.However,the rapid expansion of open source communities results in a lot of redundant contents within the community,and most importantly,among communities since they overlap each other with shared issues.On the one hand,redundant contents that are expressed in informal free texts highly increase the size of contents,which makes people suffering from finding what they exactly need from communities;on the other hand,these communities are mutually complementary that the knowledge sharing across communities can be very beneficial to users.It is crucial to recommend content for users'need through retrieving knowledge across communities.Current studies mainly focus on acquiring knowledge from one specific community to treat communities as isolated islands,and few of them have tackle the problem of content recommendation across multiple communities.In this paper,we firstly analyze five popular open source communities,and then propose an approach of crosscommunity content recommendation based on LDA topic model,integrating and distilling information from multiple communities to make knowledge acquisition easier and more efficient.Taking Docker as the case study,extensive experiments show that after performing a cross-community recommendation,more than 34% overall unanswered questions find matched answers when similarity threshold β is set to 0.85.When setting β to 0.6,almost 90% unanswered question can be answered with existing community content.It effectively leverages various communities to recommend valuable content to users.; 186-197
语种	英语
出处	International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/462457]
专题	软件与微电子学院
推荐引用方式 GB/T 7714	Yang Yong,Li Ying,Tang Hongyan,et al. An Approach for Cross-Community Content Recommendation:A Case Study on Docker. 2016-01-01.

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