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| 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 |
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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.
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Received: 16 August 2019
Published: 22 September 2020
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Corresponding Authors:
Lei WU
E-mail: smallpx@stdu.edu.cn;outhunder@126.com
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基于隐马尔科夫模型的潜在个性化路线推荐
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
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