Travel Attractions Recommendation Based on Trajectory Mining Representation Model
ZHANG Shunyao1, CHANG Liang2, GU Tianlong1, BIN Chenzhong2, SUN Yanpeng3, ZHU Guiming1, JIA Zhonghao1
1.School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004; 2.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004; 3.School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004
Abstract：A recommendation method based on the gated recurrent unit trajectory mining representation model(GRU-TMRM) is proposed to solve the problems of data sparsity and cold start in content based and collaborate filter based recommendation method, as well as the problem of ignoring rich semantics of travel track in track mining method. To take full advantage of semantics information contained in travel track, GRU-TMRM is designed. With GRU-TMRM, historical tracks of visitors can be modeled for providing personalized attractions recommendation. Experiments on real travel track dataset show that the proposed method effectively improves the accuracy and quality of recommendation compared with the widely used baseline method.
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2019, Vol. 32 
