基于隐马尔科夫模型的潜在个性化路线推荐

doi:10.3785/j.issn.1008-973X.2020.09.009

浙江大学学报(工学版)

2020, Vol. 54

Issue (9): 1736-1745 DOI: 10.3785/j.issn.1008-973X.2020.09.009

计算机技术

基于隐马尔科夫模型的潜在个性化路线推荐

潘晓(

),杨云丹,尧鑫,吴雷*(

),王书海

石家庄铁道大学经济管理学院，河北石家庄 050043

Personalized potential route recommendation based on hidden Markov model

Xiao PAN(

),Yun-dan YANG,Xin YAO,Lei WU*(

),Shu-hai WANG

School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

全文: PDF(964 KB) HTML

摘要：

现有路线大多基于历史轨迹的相似性进行推荐，容易忽略潜在新路线. 为解决这一问题，利用隐马尔科夫模型对个性化的潜在路线推荐问题进行建模，提出一种可发现隐藏路线的推荐算法（HMMPath）；根据用户指定的类别关键字序列生成访问点序列，结合路线长度、个性化路线分数以及访问点序列的可能性，为用户推荐满足个性化需求的路线；在真实签到数据集上通过改变数据集大小、查询类别关键字数量、查询类别关键字类型和推荐路线数量等参数验证所提算法的准确率和运行效率。结果表明，所提方法在包含4个以下短查询类别序列上的推荐准确率在70%以上，表现出了较好的推荐准确度.

关键词： 隐马尔科夫模型; 路线推荐; 轨迹大数据; 个性化推荐; 隐藏路线

Abstract:

Most of the existing work on route recommendation were based on the similarities among historical trajectories, however, these approaches cannot return potential routes. Thus, hidden Markov was used to model the personalized potential route recommendation problem, and a new path based on hidden Markov model (HMMPath) was proposed, which generated an access point sequence according to the user-specified category keyword sequence. A route was recommended by combining the length of the route, the personalized route score, and the possibility of accessing the sequence, so that the personalized access requirement was satisfied. Finally, experiments were performed on the real check-in data set by changing the data set size, the number of query category keywords, the type of query category keywords, and the number of recommended routes. The recommendation accuracy of the proposed method can reach more than 70% when the number of query keywords is less than 4, showing high recommendation accuracy.

Key words: hidden Markov model route recommendation trajectory big data personalized recommendation hidden routes

收稿日期: 2019-08-16 出版日期: 2020-09-22

CLC:

TP 391

基金资助: 国家自然科学基金资助项目(61303017)；河北省自然科学基金资助项目(F2018210109)；河北省教育厅重点资助项目(ZD2018040)；引进留学人员资助项目(C201822)；河北省基础研究团队资助项目(2019JT70803)；石家庄铁道大学第四届优秀青年科学基金资助项目

通讯作者: 吴雷 E-mail: smallpx@stdu.edu.cn;outhunder@126.com

作者简介: 潘晓(1981—)，女，副教授，从事数据管理、移动计算、隐私保护研究. orcid.org/0000-0002-1778-3019. E-mail: smallpx@stdu.edu.cn

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引用本文:

潘晓,杨云丹,尧鑫,吴雷,王书海. 基于隐马尔科夫模型的潜在个性化路线推荐[J]. 浙江大学学报(工学版), 2020, 54(9): 1736-1745.

Xiao PAN,Yun-dan YANG,Xin YAO,Lei WU,Shu-hai WANG. Personalized potential route recommendation based on hidden Markov model. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1736-1745.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.09.009 或 http://www.zjujournals.com/eng/CN/Y2020/V54/I9/1736

图 1 基于隐马尔科夫的模型建模

表 1

表 1 POI之间的转移概率矩阵

表 2 POI与类别文本之间的转移概率矩阵

表 3 数据集的用户、地点和轨迹统计信息

图 2 不同关键词类别的频数变化情况

表 4 类别文本及词频

图 3 不同大小的数据集上准确率和运行时间的变化情况

图 4 不同查询关键字数量下准确率和运行时间的变化情况

表 5 不同类型关键字的准确率和运行时间

图 5 不同推荐路线数量下的准确率运行时间变化情况

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