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Recommendations based on user effective point-of-interest path

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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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Authors and Affiliations

  1. College of Computer Science, Nanjing University of Posts and Telecommunication, Nanjing, 210003, People’s Republic of China

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

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  1. Guoqiang Zhou
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  2. Shuai Zhang
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  3. Yi Fan
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  4. Jingjin Li
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  5. Wenbo Yao
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  6. Hongfang Liu
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Correspondence to Guoqiang Zhou.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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Zhou, G., Zhang, S., Fan, Y. et al. Recommendations based on user effective point-of-interest path. Int. J. Mach. Learn. & Cyber. 10, 2887–2899 (2019). https://doi.org/10.1007/s13042-018-00910-5

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  • DOI: https://doi.org/10.1007/s13042-018-00910-5

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