Tourism Recommendation Method Based on Tensor Decomposition and Its Application
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Di Gao, Huili Yan, Xinyi Xu
Recommendation system is a popular Internet application system, which includes software tools and technologies that provide advice for users to choose products. When the user labels, this function can automatically provide some tag lists that the user may be interested in or related to for the user to choose to use. To solve the problems of complex and changeable user context and sparse data in tourism recommendation, a tensor decomposition recommendation algorithm based on mobile user context similarity is proposed. Combining the user's active interest tendency with the user's browsing behavior, the comprehensive interest is analyzed. According to the rating information of neighboring users with similar interests to the target users, the user's rating of the scenic spots to be recommended is predicted, and the scenic spots with the top n predicted scores are selected for recommendation.
Tensor decomposition, Tourist attractions, User context