Rohan Tondulkar
ABSTRACT
There has been a massive rise in the use of Community Question and Answering (CQA) forums to get solutions to various technical and non-technical queries. One common problem faced in CQA is the small number of experts, which leaves many questions unanswered. This paper addresses the challenging problem of predicting the best answerer for a new question and thereby recommending the best expert for the same. Although there are work in the literature that aim to find possible answerers for questions posted in CQA, very few algorithms exist for finding the best answerer whose answer will satisfy the information need of the original Poster. For finding answerers, existing approaches mostly use features based on content and tags associated with the questions. There are few approaches that additionally consider the users' history. In this paper, we propose an approach that considers a comprehensive set of features including but not limited to text representation, tag based similarity as well as multiple user-based features that target users' availability, agility as well as expertise for predicting the best answerer for a given question. We also include features that give incentives to users who answer less but more important questions over those who answer a lot of questions of less importance. A learning to rank algorithm is used to find the weight of each feature. Experiments conducted on a real dataset from Stack Exchange show the efficacy of the proposed method in terms of multiple evaluation metrics for accuracy, robustness and real time performance.
- Berger A., Caruana R., Cohn D. and Freitag D., and Mittal V. 2000. Bridging the lexical chasm: statistical approaches to answer-finding. In 23rd annual international ACM SIGIR conference on Research and development in information retrieval. ACM. Google Scholar
Digital Library
- Yang B. and Manandhar S. 2014. Tag-based expert recommendation in community question answering. In Advances in Social Networks Analysis and Mining (ASONAM). IEEE/ACM. Google ScholarDigital Library
- Burges C.J. 2010. From ranknet to lambdarank to lambdamart: An overview. Learning. ACM, 475--482.Google Scholar
- Yang D., Adamson D., and Rose C.P. 2014. Question Recommendation with Constraints for Massive Open Online Courses. In 8th ACM Conference on Recommender systems. ACM. Google ScholarDigital Library
- Riahi F., Zolaktaf Z., Shafiei M., and Milios E. 2012. Finding expert users in community question answering. In 21st International Conference on World Wide Web. ACM. Google ScholarDigital Library
- Dong H., Wang J., Lin H., Xu B., and Yang Z. 2005. Predicting best answerers for new questions: an approach leveraging distributed representations of words in community question answering. In Frontier of Computer Science and Technology (FCST). IEEE. Google ScholarDigital Library
- Jeon J., Croft W.B., Lee J.H., and Park S. 2006. A Framework to Predict the Quality of Answers with NonTextual Features. In 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM. Google ScholarDigital Library
- Yang J., Tao K., Bozzon A., and Houben G.J. 2014. Sparrows and owls: Characterisation of expert behaviour in stackoverflow. In International Conference on User Modeling, Adaptation, and Personalization. Springer, Cham., 266--277.Google Scholar
- J. Jeo, Croft W.B., and Lee. 2005. Finding Similar Questions in Large Question and Answer Archives. In 14th ACM international conference on Information and knowledge management. ACM. Google ScholarDigital Library
- Bae K. and Ko Y. 2017. Improving Question Retrieval in cQA Services Using a Dependency Parser. In IEICE TRANSACTIONS on Information and Systems. 807--810.Google Scholar
- Yang L., Qiu M., Gottipati S., Zhu F., Jiang J., Sun H., and Chen Z. 2013. Cqarank: jointly model topics and expertise in community question answering. In Proceedings of the 22nd ACM international conference on Information and Knowledge Management. ACM, 99--108. Google ScholarDigital Library
- Qu M., Qiu G., He X., Zhang C., Wu H., Bu J., and Chen. 2009. Probabilistic Question Recommendation for Question Answering Communities. In 18th international conference on World wide web. ACM. Google ScholarDigital Library
- Roy P.K., Ahmad Z., J.P. Alryalat Singh, M.A.A. Rana, N.P., and Dwivedi Y.K. 2018. Finding and Ranking High-Quality Answers in Community Question Answering Sites. In Global Journal of Flexible Systems Management. 53--68.Google Scholar
- Le Q. and Mikolov T. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning. 1188--1196. Google ScholarDigital Library
- Liu X., Croft W.B., and Koll M. 2005. Finding Experts in Community-Based Question-Answering Services. In 14th ACM international conference on Information and knowledge management. ACM. Google ScholarDigital Library
- Xue X., Jeon J., and Croft W.B. 2008. Retrieval models for question and answer archives. In 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 475--482. Google ScholarDigital Library
- Chen Z. and Zhang C. and Zhao Z. and Cai D. 2016. Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning. arXiv:1611.08135Google Scholar
Index Terms
- Get me the best: predicting best answerers in community question answering sites
Recommendations
CQArank: jointly model topics and expertise in community question answering
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge ManagementCommunity Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it is critically important for CQA services ...
MATER: Bi-level matching-aggregation model for time-aware expert recommendation
AbstractCommunity question answering (CQA) websites have grown rapidly, but they face a gap between questions and answerers. This gap causes delays in getting answers and overwhelms potential answerers. Expert recommendation (ER) can bridge this gap by ...
Dynamic user modeling for expert recommendation in community question answering
Community Question Answering (CQA) websites has become an important channel for people to acquire knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, i.e., expert recommendation. ...
Comments