| Summary: | 碩士 === 國立交通大學 === 運輸與物流管理學系 === 103 === Recently, the change of tourism and consumption type make the tourism in business district vigorous. Besides satisfying the necessary of residents, business district has transformed toward the tourism aspect. With the expand of business district scale, the companies and the shops are getting more and more various, so the information consumers receive are so complicated. If the consumers would like a fine and complete tour, they often have to aggregate a large amount of information to decide their tour that makes their time and spiritual cost increase. However, in the time people emphasize personalization, if we can consider the preference for different consumers and provide a one-to-one tour recommendation service, consumers can have less cost for tourism and maximize their utility to increase their motivation for tourism and consumption when they visit the business district. And indirectly, it can develop the business opportunities to promote the economy and development of the area of business district.
The purpose of this research is developing a personalized recommender system for tours in business district, demonstrating it by the data of Xinyi Business District, and proceduring a questionnaire to collect the advice of system improvement. The research object is users who the system has his or her historical data, and the platform of this system is mobile device. This system mainly has four modules, which is sequentially analysis of attribute preference, collaborative filtering, selection with contextual data and data imported by users and tour planning finally. At first, the system will analyze attribute preference of users through four kinds of data and find out the preference value of POIs for the specific user collaborative filtering. Then it uses the contextual data and data imported by the specific user to filter and select POI he or she likes. After the POI list are completed, the system will plan the tour by our algorithm and present it to users. The system combines text mining techniques, collaborative filtering, content-based filtering and solve the rating sparsity problem which normal recommender systems meet. It calculates the similarity by attribute preference of users so that its calculating time won’t rise severely when the number of POIs increases. It also use text mining to analyze text comments to transfer into preference value. Besides, the system emphasize the context of users by exploiting contextual data and data imported by users so that the tour this system recommend can match the preference and demand of users!
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