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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 10/2019

16-01-2019 | Original Article

Recommendations based on user effective point-of-interest path

Authors: Guoqiang Zhou, Shuai Zhang, Yi Fan, Jingjin Li, Wenbo Yao, Hongfang Liu

Published in: International Journal of Machine Learning and Cybernetics | Issue 10/2019

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Abstract

Point-of-interest (POI) recommendation has become an important service in location-based social networks. Existing recommendation algorithms provide users with a diverse pool of POIs. However, these algorithms tend to generate a list of unrelated POIs that user cannot continuously visit due to lack of appropriate associations. In this paper, we first proposed a concept that can recommend POIs by considering both category diversity features of POIs and possible associations of POIs. Then, we developed a top-k POI recommendation model based on effective path coverage. Moreover, considering this model has been proven to be a NP-hard problem, we developed a dynamic optimization algorithm to provide an approximate solution. Finally, we compared it with two popular algorithms by using two real-world datasets, and found that our proposed algorithm has better performance in terms of diversity and precision.
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Metadata
Title
Recommendations based on user effective point-of-interest path
Authors
Guoqiang Zhou
Shuai Zhang
Yi Fan
Jingjin Li
Wenbo Yao
Hongfang Liu
Publication date
16-01-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 10/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-00910-5

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