Personalized potential route recommendation based on hidden Markov model

被引：0
作者：
Pan X. [1 ]
Yang Y.-D. [1 ]
Yao X. [1 ]
Wu L. [1 ]
Wang S.-H. [1 ]
机构：
[1] School of Economics and Management, Shijiazhuang Tiedao University, Shijiazhuang
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2020年 / 54卷 / 09期
关键词：
Hidden Markov model; Hidden routes; Personalized recommendation; Route recommendation; Trajectory big data;
D O I：
10.3785/j.issn.1008-973X.2020.09.009
中图分类号：
学科分类号：
摘要：
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. Copyright ©2020 Journal of Zhejiang University (Engineering Science). All rights reserved.
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页码：1736 / 1745
页数：9
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