| Summary: | 博士 === 國立交通大學 === 資訊管理研究所 === 102 === With the emergence of Social Media, Social Question-Answering (SQA) websites have become common knowledge production and sharing platforms. This platform provides knowledge community services where users with common interests, needs or expertise can form a knowledge community. Community members can collect and share QA knowledge (documents) regarding their interests. However, due to the massive amount of QAs created every day, information overload can become a major problem. Consequently, a recommender system is needed to suggest QA documents for communities of SQA websites.
In this thesis, we propose several novel methods, called GTPR-based approaches, to recommend related QA documents for knowledge communities of SQA sites. The proposed methods recommend QA documents by considering community-specific features, the relationships between knowledge documents, and documents’ relevance to the communities. In addition, due to the robustness problem of the existing topic grouping method using user-defined tags in Social Media, this study further propose a novel method, called GPTLR, incorporating the community’s latent topics of interest and collection weights based on members’ topical reputations to improve content-based recommendation models.
This research evaluates and compares the proposed methods using a real-world dataset collected from a SQA website. Experimental results show that the proposed methods outperform other traditional methods, providing a more effective and accurate recommendations of Q&;A documents to knowledge communities.
|
|---|
