Recommending Travel Threads Based on Information Need Model

• Main Authors: Po-ling Chen, 陳柏伶
• Other Authors: San-Yih Hwang
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/53239987372283131768

碩士 === 國立中山大學 === 資訊管理學系研究所 === 100 === Recommendation techniques are developed to discover user’s real information need among large amounts of information. Recommendation systems help users filter out information and attempt to present those similar items according to user’s tastes. In our work, we focus on discussion threads recommendation in the tourism domain. We assume that when users have traveling information need, they will try to search related information on the websites. In addition to browsing others suggestions and opinions, users are allowed to express their need as a question. Hence, we focus on recommending users previous discussion threads that may provide good answers to the users’ questions by considering the question input as well as their browsing records. We propose a model, which consists of four perspectives: goal similarity, content similarity, freshness and quality. To validate and the effectiveness of our model on recommendation performance, we collected 14348 threads from TripAdvisor.com, the largest travel website, and recruited ten volunteers, who have interests in the tourism, to verify our approach. The four perspectives are utilized by two methods. The first is Question-based method, which makes use of content similarity, freshness and quality and the second is Session-based method, which involves goal similarity. We also integrate the two methods into a hybrid method. The experiment results show that the hybrid method generally has better performance than the other two methods.