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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摘要针对旅游推荐系统中基于内容的推荐和基于协同过滤的推荐方法的数据稀疏性和冷启动问题,以及现有轨迹挖掘方法忽略旅游轨迹中高级语义的问题,提出基于门控循环单元轨迹挖掘模型的推荐方法.为了充分利用旅游轨迹的高级语义信息,基于循环神经网络设计轨迹挖掘表示模型,对游客的旅游轨迹进行建模,在利用游客历史轨迹建模后向游客提供个性化旅游景点推荐.在真实旅游轨迹数据集上的实验表明,相比广泛使用的基线方法,文中方法在景点推荐的准确性和质量上都有一定提高.

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	张舜尧
	常亮
	古天龙
	宾辰忠
	孙彦鹏
	朱桂明
	贾中浩

关键词 ：推荐系统, 旅游推荐, 旅游轨迹, 门控循环单元轨迹挖掘表示模型

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

收稿日期: 2018-08-13

ZTFLH:

TP 311

基金资助:国家自然科学基金项目(No.U1501252,61572146)、广西自然科学基金项目(No.2016GXNSFDA380006)、广西创新驱动重大专项项目(No.AA17202024)、广西信息科学实验中心平台建设项目(No.PT1601)、广西高校中青年教师基础能力提升项目(No.2018KYD203)、广西可信软件重点实验课题(No.KX201729)资助

通讯作者: 宾辰忠(通讯作者),博士研究生,讲师,主要研究方向为数据挖掘、智能推荐.E-mail:cz_bin@guet.edu.cn.

作者简介: 张舜尧,硕士研究生,主要研究方向为机器学习、知识图谱、推荐系统.E-mail:806082225@qq.com.常亮,博士,教授,主要研究方向为知识工程、符号推理.E-mail:changl@guet.edu.cn.古天龙,博士,教授,主要研究方向为知识工程、符号推理.E-mail:cctlgu@guet.edu.cn.孙彦鹏,硕士研究生,主要研究方向为机器学习、数据挖掘、推荐系统.E-mail:yanpeng_sun@yeah.net.朱桂明,硕士研究生,主要研究方向为机器学习、推荐系统、知识图谱.E-mail:18770914075@163.com.贾中浩,硕士研究生,主要研究方向为机器学习、推荐系统、知识图谱.E-il:1090994959@qq.com.

引用本文:

张舜尧, 常亮, 古天龙, 宾辰忠, 孙彦鹏, 朱桂明, 贾中浩. 基于轨迹挖掘模型的旅游景点推荐[J]. 模式识别与人工智能, 2019, 32(5): 463-471. ZHANG Shunyao¹, CHANG Liang², GU Tianlong¹, BIN Chenzhong²,
SUN Yanpeng³, ZHU Guiming¹, JIA Zhonghao¹. Travel Attractions Recommendation Based on Trajectory Mining Representation Model. , 2019, 32(5): 463-471.

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

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