Applying Tag Recommendation base on Document Similarity in Question and Answer Website

• Main Authors: Tsao, Pin Yeh, 葉早彬
• Other Authors: 楊建民
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/34szqu

碩士 === 國立政治大學 === 資訊管理研究所 === 103 === With User's behavior change. User access to new knowledge from the internet instead of from the traditional media. This Change leads to a lot new behavior. Social tagging is popular in recent years through a user tag to classify and annotate information. Unlike traditional taxonomy requiring items are classified into predefined categories, Social tagging is more elastic to adjust through the content change. Q &; A Website is the rise in recent years. Like Quora , Stack Overflow , yahoo Knowledge plus. User can interact with other people form this platform , in Q &; A discussion, with People's experience and expertise to help the user find a satisfactory answer. This study hopes to build a tag recommendation system for Q &; A Website. The recommendation system can help people find the right problem efficiently , and let Q &; A platform can put these numerous problems into the right place. We collect 20,638 questions from Stack Exchange. Use naïve Bayes algorithm and document similarity calculation to recommend tag for the new document. The result of the evaluation show we can effectively recommend relevant tags for the new question.