Expert as a service: Software expert recommendation via knowledge domain embeddings in stack overflow
Conference Proceeding Article
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.
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 as a Service, Expert recommendations, Expertise finding, Graph-based clustering, Matrix factorizations, Question Answering, Stack overflow, Web services
Programming Languages and Compilers | Software Engineering
Proceedings of the 24th International Conference on Web Services, ICWS 2017, Honolulu, United States, June 25-30
Institute of Electrical and Electronics Engineers Inc.
City or Country
Honolulu, United States
HUANG, Chaoran; YAO, Lina; WANG, Xianzhi; BENATALLAH, Boualem; and SHENG, Quan Z..
Expert as a service: Software expert recommendation via knowledge domain embeddings in stack overflow. (2017). Proceedings of the 24th International Conference on Web Services, ICWS 2017, Honolulu, United States, June 25-30. 317-324. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3860