基于多重隐语义表示模型的旅游路线挖掘

doi:10.16451/j.cnki.issn1003-6059.201805008

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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.

Key words： Travel Route Mining Travel Route Mining Latent Semantic Latent Semantic Multiple Latent Semantic Multiple Latent Semantic Attraction Recommendation Attraction Recommendation

Received: 05 February 2018

ZTFLH:

TP 311

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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	SUN Yanpeng
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	BIN Chenzhong
	BIN Chenzhong
	SUN Lei
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Cite this article:

SUN Yanpeng,SUN Yanpeng,GU Tianlong等. Travel Routing Mining Based on Multiple Latent Semantic Representation Model[J]. , 2018, 31(5): 462-469.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201805008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I5/462

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