From discussion to wisdom: web resource recommendation for hyperlinks in stack overflow

Jing Li, Zhenchang Xing, Deheng Ye, Xuejiao Zhao

Abstract: Stack Overflow has been providing question and answering service for 7 years. It has become a tremendous knowledge repository for developers' thoughts and practices. Hyperlinks in discussion threads of Stack Overflow are essential knowledge entities for programming on the Web, such as a software library, an API documentation, a code example, or a tutorial. Tens of millions of hyperlinks are disseminated in Stack Overflow, while wisdom on what web resources have been highly recognized by the community is implicit in millions of discussion threads. In this paper, we develop the WisLinker framework that extracts knowledge from discussion, then turns knowledge into wisdom by learning through the knowledge dissemination history. With this wisdom, for a specific hyperlink that users are concerned with, WisLinker can recommend web resources highly recognized by the Stack Overflow community. We evaluate the validity of WisLinker in an open-ended setting using Stack Overflow data dump. We also implement a browser extension for live recommendation of web resources while users browse web pages. WisLinker could enable more efficient exploratory search and information discovery of programming-related web resources.