Expert as a service: Software expert recommendation via knowledge domain embeddings in stack overflow

• Main Authors: HUANG, Chaoran, YAO, Lina, WANG, Xianzhi, BENATALLAH, Boualem, SHENG, Quan Z.
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2017
• 主題: Expert as a Service; Expertise finding; Knowledge discovery; Question answering; Stack Overflow; Cluster analysis; Computer programming; Data mining; Factorization; Graphic methods; Matrix algebra; Natural language processing systems; Semantics; Websites; Collaborative network; Expert recommendations; Graph-based clustering; Matrix factorizations; Question Answering; Stack overflow; Web services; Programming Languages and Compilers; Software Engineering
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/4046
• 機構: Singapore Management University

Question answering (Q&A) communities have gained momentum recently as an effective means of knowledge sharing over the crowds, where many users are experts in the real-world and can make quality contributions in certain domains or technologies. Although the massive user-generated Q&A data present a valuable source of human knowledge, a related challenging issue is how to find those expert users effectively. In this paper, we propose a framework for finding such experts in a collaborative network. Accredited with recent works on distributed word representations, we are able to summarize text chunks from the semantics perspective and infer knowledge domains by clustering pre-trained word vectors. In particular, we exploit a graph-based clustering method for knowledge domain extraction and discern the shared latent factors using matrix factorization techniques. The proposed clustering method features requiring no post-processing of clustering indicators and the matrix factorization method is combined with the semantic similarity of the historical answers to conduct expertise ranking of users given a query. We use Stack Overflow, a website with a large group of users and a large number of posts on topics related to computer programming, to evaluate the proposed approach and conduct extensively experiments to show the effectiveness of our approach.