Expert Finding in Community Question-Answering for Post Recommendation

Abstract

Community question answering system is a perfect example of platform where people participate to seek expertise on their topic of interest. But information overload, finding the expertise level of users and trustworthy answers remain key challenges within these communities. Moreover, people do not look for personal advices but expert views on such platforms therefore; expert finding is an integral part of these communities. In order to trust someone's opinion who is not known in person by the users of the community, it is necessary to find the credibility of such person. By determining expertise levels of users, authenticity of their posts can easily be determined. Also, by identifying experts, each expert will be shown relevant posts to indulge in so that he can use his knowledge and skills to give valid and correct answers. For users too, it will be easy to find reliable answers, once they get to know the expertise level of the answerers. Motivated by these facts, we put forward a framework for finding experts in online question answer community (stackoverflow) referred to as Expert Recommender System which uses a well-recognized global-trust metric, PageRankTM for finding experts in the community building a Trust-based system and then uses collaborative filtering to find similar experts based on their level of expertise and their topics of interests to a particular user. Once we have the top- k similar experts to a given expert, that expert is recommended with posts to collaborate upon, based on activities done by his top-k neighbor experts. The framework is evaluated for its performance and it clearly indicates the effectiveness of the system.