Paper Info
Title
LinkLive: discovering Web learning resources for developers from Q&A discussions
Abstract
Software developers need access to correlated information (e.g., API documentation, Wikipedia pages, Stack Overflow questions and answers) which are often dispersed among different Web resources. This paper is concerned with the situation where a developer is visiting a Web page, but at the same time is willing to explore correlated Web resources to extend his/her knowledge or to satisfy his/her curiosity. Specifically, we present an item-based collaborative filtering technique, named LinkLive, for automatically recommending a list of correlated Web resources for a particular Web page. The recommendation is done by exploiting hyperlink associations from the crowdsourced knowledge on Stack Overflow. We motivate our research using an exploratory study of hyperlink dissemination patterns on Stack Overflow. We then present our LinkLive technique that uses multiple features, including hyperlink co-occurrences in Q&A discussions, locations (e.g., question, answer, or comment) in which hyperlinks are referenced, and votes for posts/comments in which hyperlinks are referenced. Experiments using 7 years of Stack Overflow data show that, our technique recommends correlated Web resources with promising accuracy in an open setting. A user study of 6 participants suggests that practitioners find the recommended Web resources useful for Web discovery.
Year
DOI
Venue
2019
10.1007/s11280-018-0621-y
World Wide Web
Keywords
Field
DocType
API documentation, Recommendation systems, Web discovery, Social media
Web resource,Recommender system,World Wide Web,Social media,Collaborative filtering,Web page,Computer science,Application programming interface,Artificial intelligence,Hyperlink,Exploratory research,Machine learning
Journal
Volume
Issue
ISSN
22.0
4
1573-1413
Citations 
PageRank 
References 
1
0.35
82
Authors
3
Name
Order
Citations
PageRank
Jing Li110813.74
Zhenchang Xing2138787.95
Aixin Sun33071156.89