知識社群問答文件推薦技術

Abstract

隨著社交媒體的興起，社會問答網站已成為常見的知識生產與分享平台。此平台提供之社群服務讓有著共同興趣、需求或專長的使用者可以組成知識社群，而社群成員可以收藏以及分享他們感興趣的問答知識（文件）。然而，每天產生之大量問答文件，使得資訊過載成為重要的問題，因此有必要發展推薦系統來為社會問答網站之社群建議所需之問答知識文件。 本篇論文提出了幾個稱為GTPR為基礎之新穎推薦方法，將相關的問答文件推薦給社會問答網站中的知識社群。提出的方法於推薦問答文件時，考量了幾個社群相關的特徵、知識文件之間的關係以及文件和社群的相關性。此外，由於現有採用社交媒體之使用者定義標籤以劃分文件主題的方法有著穩健性不足之問題，本研究進一步提出稱為GPTLR之方法，結合社群之潛藏主題興趣以及基於成員主題聲譽之收藏權重來改善以內容為基礎之推薦模型。 本研究使用了真實的社會問答網站資料，以評估與比較所提出之方法。實驗結果顯示提出之方法優於其它傳統方法，提供了更有效的方式為知識社群推薦問答文件。

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.