Abstract
In this paper, we propose a method to recommend to a tourist (user) such a travel destination that is little known to many people, but of interesting for the user. To this end, we use two recommendation techniques, i.e. collaborative filtering and content-based filtering. We use the collaborative filtering method to predict the user’s preference and select a destination that is well known and of interesting for the user. Then, with the destination as a clue, we make a final recommendation by finding out such a destination that is similar to the clue, but not well known itself by means of the content-based filtering method. To characterize travel destinations, we focus on many pieces of word-of-mouth information about them on the Internet, and use tf-idf values of keywords appearing in them to construct feature vectors for destinations. We conduct a user study and show that the proposed method is promising.
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Ohara, K., Fujimoto, Y., Shiina, T. (2010). Recommendation of Little Known Good Travel Destinations Using Word-of-Mouth Information on the Web. In: An, A., Lingras, P., Petty, S., Huang, R. (eds) Active Media Technology. AMT 2010. Lecture Notes in Computer Science, vol 6335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15470-6_20
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DOI: https://doi.org/10.1007/978-3-642-15470-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15469-0
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