基于轨迹挖掘模型的旅游景点推荐

doi:10.16451/j.cnki.issn1003-6059.201905009

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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.

Key words： Recommendation System Travel Recommendation Travel Track Gated Recurrent Unit Trajectory Mining Representation Model

Received: 13 August 2018

ZTFLH:

TP 311

Fund:Supported by National Natural Science Foundation of China(No.U1501252,61572146), Natural Science Foundation of Guangxi Province(No.2016GXNSFDA380006), Innovation-Driven Major Projects of Guangxi Province(No.AA17202024), Guangxi Information Science Experiment Center Platform Construction Project(No.PT1601),Guangxi University Young and Middle-Aged Teachers Basic Ability Improvement Project(No.2018KYD203),
Guangxi Trusted Software Key Experimental Funding Project(No.KX201729)

Corresponding Authors: (BIN Chenzhong(Corresponding author), Ph.D. candidate, lecturer. His research interests include data mining and intelligent re-commendation.)

About author:: (ZHANG Shunyao, master student. His research interests include machine learning, knowledge graph and recommendation system.)(CHANG Liang, Ph.D., professor. His research interests include knowledge enginee-ring and symbolic reasoning.)(GU Tianlong, Ph.D., professor. His research interests include knowledge enginee-ring and symbolic reasoning.)(SUN Yanpeng, master student. His research interests include machine learning, data mining and recommendation system.)(ZHU Guiming, master student. His research interests include machine learning, recommendation system and knowledge graph.)(JIA Zhonghao, master student. His research interests include machine learning, recommendation system and knowledge graph.)

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	Articles by authors
	ZHANG Shunyao¹
	CHANG Liang²
	GU Tianlong¹
	BIN Chenzhong²
	SUN Yanpeng³
	ZHU Guiming¹
	JIA Zhonghao¹

Cite this article:

ZHANG Shunyao¹,CHANG Liang²,GU Tianlong¹等. Travel Attractions Recommendation Based on Trajectory Mining Representation Model[J]. , 2019, 32(5): 463-471.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201905009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I5/463

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