Abstract
To solve the problem of low accuracy of traditional travel route recommendation algorithm, a travel route recommendation algorithm based on interest theme and distance matching is proposed in this paper. Firstly, the real historical travel footprints of users are obtained through analysis. Then, the user’s preferences of interest theme and distance matching are proposed based on the user’s stay in each scenic spot. Finally, the optimal travel route calculation method is designed under the given travel time limit, starting point, and end point. Experiments on the real data set of the Flickr social network showed that the proposed algorithm has a higher accuracy rate and recall rate, compared with the traditional algorithm that only considers the interest theme and the algorithm which only considers the distance matching.
Highlights
In recent years, the research of recommender system has developed rapidly
According to the Point of interest (POI) set P, time budget B, starting point POIp1, and ending point POIpn, the route with the highest score is recommended to users by using the proposed algorithm which combines user interest preferences and POI distance based on the orientation problem
In the traditional travel route recommendation algorithm, POI distance and user interest preference are considered as the criteria
Summary
The research of recommender system has developed rapidly. Various recommendation systems are widely used in e-commerce, social networking sites, e-tourism, Internet advertising, and many other fields, and these recommendation systems show superior effects and prospects [1,2,3]. With the rise of more and more online travel websites (such as Expedia, Travelzoo, tuniu), more and more online data can describe users’ interests and preferences This makes tourism product recommendation become one of the hotspots of recommendation system research [4, 5]. The abovementioned recommendation technologies solve the problem of travel recommendation to a certain extent This recommendation technology is only suitable for tourism data with relatively simple data structure, or relies on geographic information data, so it is difficult to fully capture users’ real-time interest preferences. In social network trajectory data mining, applications mainly include location recommendation, path recommendation, and behavior preference recommendation [15]. There are many processing methods of social network data mining, including clustering, classification, and other traditional technologies. In the personalized travel route recommendation based on group pattern, due to the lack of semantic information, the traditional group pattern mining leads to an incomplete personalized recommendation
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