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In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method. <\/jats:p>","DOI":"10.1142\/s0218126622500396","type":"journal-article","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T02:10:50Z","timestamp":1630721450000},"source":"Crossref","is-referenced-by-count":1,"title":["A New Self-Adaptive Hybrid Markov Topic Model Poi Recommendation in Social Networks"],"prefix":"10.1142","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5371-3256","authenticated-orcid":false,"given":"Bin","family":"Xu","sequence":"first","affiliation":[{"name":"School of Management Science and Engineering, Anhui University of Technology, Ma\u2019anshan, P. R. China"},{"name":"Key Laboratory of Multidisciplinary Management, Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma\u2019anshan, P. R. 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