SNS opinion-based recommendation for eTourism: A Taipei restaurant example

Abstract

By the use of Internet technology in the travel and tourism industry, tourists are considered to play more significant role in the process of planning and designing tourism-related products and services. The amount of information that can acquire from Internet may far exceed one can handle, and makes the decision considerations in the travel planning process fairly complicated. Yu [3] proposed an integrated functional framework and design process for providing web-based personalized and community decision support services, and argue to extract user experiences by using case-based reasoning. However, to construct patterns from case-based reasoning among gigantic amount of user-generated content is a heavy-loading task. In this study, we adopted latent semantic analysis (LSA) [8, 9], which is constructed language pattern and discover semantic relationship between topics in big data scenario, to recommend restaurant according to desiring for similar experience. Both academic and practical implications of proposed approach are also discussed.