标题: | 问答网站社群之互补问答知识文件推荐 Complementary Q&A Document Recommendations for Communities of Question-Answering Websites |
作者: | 黄群凯 Huang, Chun-Kai 刘敦仁 Liu, Duen-Ren 资讯管理研究所 |
关键字: | 知识社群;群体推荐;知识互补;知识品质;问答网站;链结分析;知识声望;Knowledge Community;Group Recommendation;Knowledge Complementation;Knowledge Quality;Question-Answering Websites;Link Analysis;Knowledge Reputation |
公开日期: | 2011 |
摘要: | 随着网路科技及Web2.0概念的蓬勃发展,问答网站逐渐成为重要的知识分享平台。问答网站提供知识社群的服务机制,让拥有共同兴趣或专长的使用者组成知识社群。社群中成员能收藏有兴趣之问答知识,并分享与社群相关的知识议题。 然而问答网站每天有大量的问答知识产生,造成了资讯过量的问题,因此社群知识收藏之推荐机制应运而生,用以推荐知识社群相关有兴趣之问答知识。然目前相关文献少有针对问答网站社群知识收藏的群体推荐机制之研究。而传统群体推荐机制多是以群体成员之重要性作为权重,结合各单独成员之兴趣特征档以产生群体兴趣特征档,进而以群体兴趣特征档过滤推荐物件,并未考量推荐物件如问答知识之品质、知识文件之相关互补性,以及社群成员收藏知识之声望等因素。 本研究提出问答网站社群知识收藏之群体推荐机制,以推荐社群相关且有兴趣的问答知识文件。所提的推荐方法主要以社群中历史收藏知识之推荐分数、收藏时间及知识成员之重要性包括收藏知识声望与回答知识声望等,再根据不同的知识主题产生社群群体兴趣特征档,并考量知识文件之间的相关互补性,与知识文件之品质,进而推荐具品质之社群相关互补问答文件集,以满足社群成员对于问答知识的需求,促进知识分享的交流。 最后本研究以奇摩知识家问答网站做为实验评估的资料来源,实验结果显示本研究所提出的方法比传统方法能更有效的针对知识社群推荐与其兴趣相关的知识文件。 With the rapid development of Internet and Web 2.0 technology, Question & Answering (Q&A) websites have become an essential knowledge sharing platform. This platform provides knowledge community services where allows users with common interests or expertise to form a knowledge community. Community members can collect and share Q&A knowledge (documents) of their interests. However, due to the massive amount of Q&A documents created every day, information overload become a main problems. Consequently, a group-based recommendation mechanism is needed to recommend Q&A documents for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. Traditional group-based recommendation methods use member importance as weight to consolidate individual profiles and generate group profiles, which in turn are used to filter out items of recommendation, but do not consider certain factors of recommended items, such as the quality of documents, the reputation of community members, and the complementary relationships between documents. In this study, we will propose novel group-based recommendation methods to recommend related Q&A documents for knowledge communities of Q&A websites. The proposed recommendation method build several community topic profiles by considering factors such as community members’ reputations in collecting and answering Q&A documents, push (recommendation) scores of Q&A documents and the collected time of Q&A document from the historical collected Q&A documents, and make recommendations via considering the quality of Q&A documents and their relevance to the communities. Moreover, we further investigate the methods for analyzing and recommending complementary Q&A document sets to satisfy community members’ knowledge needs and facilitate knowledge sharing in communities. This research will evaluate and compare the proposed methods by using the experimental dataset collected from the Yahoo! Answers Taiwan website. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner in recommending Q&A documents to knowledge communities. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT079934515 http://hdl.handle.net/11536/50139 |
显示于类别: | Thesis |