Travel Routing Mining Based on Multiple Latent Semantic Representation Model
SUN Yanpeng1, GU Tianlong2, BIN Chenzhong2 , SUN Lei2
1.School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004 2.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004
Abstract Aiming at mining and recommending the personalized travel behavior of tourists, a multiple latent semantic travel route representation model(MLSTR-RM) is proposed. With the consideration of the influence of different contexts on the travel route, the efficient representation of different latent semantics in travel routes is studied in MLSTR-RM. Firstly, the latent semantic contained by the different contexts in model is determined. Then, the negative sampling is applied to train parameters in the model, and a personalized attraction recommendation method is designed based on MLSTR-RM model. Experiments on real data sets show the effectiveness of the proposed model.
Corresponding Authors:
BIN Chenzhong, Ph.D. candidate, lecturer. His research inte-rests include data mining and intelligent recommendation.
About author:: SUN Yanpeng, master student. His research interests include machine learning, data mining and recommendation system. GU Tianlong, Ph.D., professor. His research interests include knowledge engineering and symbolic reasoning. SUN Lei, master student. His research interests include location awareness, pattern mi-ning and intelligent recommendation.
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2018, Vol. 31 
