Recommending Best Answer in a Collaborative Question Answering System
RECOMMENDING BEST ANSWER IN A COLLABORATIVE QUESTION ANSWERING SYSTEM Mrs Lin Chen Submitted in fulfilment of the requirements for the degree of Master of Information Technology (Research) School of Information Technology Faculty of Science & Technology Queensland University of Technology 2009 Recommending Best Answer in a Collaborative Question Answering System Page i Keywords Authority, Collaborative Social Network, Content Analysis, Link Analysis, Natural Language Processing, Non-Content Analysis, Online Question Answering Portal, Prestige, Question Answering System, Recommending Best Answer, Social Network Analysis, Yahoo! Answers. © 2009 Lin Chen Page i Recommending Best Answer in a Collaborative Question Answering System Page ii © 2009 Lin Chen Page ii Recommending Best Answer in a Collaborative Question Answering System Page iii Abstract The World Wide Web has become a medium for people to share information. People use Web- based collaborative tools such as question answering (QA) portals, blogs/forums, email and instant messaging to acquire information and to form online-based communities. In an online QA portal, a user asks a question and other users can provide answers based on their knowledge, with the question usually being answered by many users. It can become overwhelming and/or time/resource consuming for a user to read all of the answers provided for a given question. Thus, there exists a need for a mechanism to rank the provided answers so users can focus on only reading good quality answers. The majority of online QA systems use user feedback to rank users’ answers and the user who asked the question can decide on the best answer. Other users who didn’t participate in answering the question can also vote to determine the best answer.
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