Travel Routing Mining Based on Multiple Latent Semantic Representation Model
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.
NETL
NSTL
万方数据
维普
