Anandhan, Anitha and Ismail, Maizatul Akmar and Mohd Shuib, Nor Liyana (2022) Expert recommendation through tag relationship in community question answering. Malaysian Journal of Computer Science, 35 (3). pp. 201-221. ISSN 0127-9084, DOI https://doi.org/10.22452/mjcs.vol35no3.2.
Full text not available from this repository.Abstract
Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users' domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts' derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the ``Tag Relationship Expert Recommendation (TRER)'' method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user's question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Recommender system; Matrix factorisation; Online community; Metadata; Social network; Expert profiling; Tag relationship; User profile |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Information System |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 25 Oct 2023 01:34 |
| Last Modified: | 25 Oct 2023 01:34 |
| URI: | http://eprints.um.edu.my/id/eprint/41780 |
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