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An ensemble learning model for preference-geographical aware point-of interest recommendation

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Abstract

The emergence of location-based social networks (LBSNs), which contain a lot of information, creates the possibility of a point-of-interest (POI) recommendation. Meanwhile, LBSNs also make the POI recommendation an important service and have attracted widespread attention from industries and academia. Most traditional POI recommendation methods focus on finding similar users of a target user and generate suggestions by exploring check-in histories of these similar users. However, such suggestions may be biased and lack variousness. To address this problem, we design a novel ensemble learning framework for POI recommendation, named Preference-Geographical Point-of-interest Recommendation Ensemble (PG-PRE). For a target user, we first construct multiple similar user group and use a roulette selection-based sampling method to improve the variousness of such groups. Each group will give a POI recommendation suggestion. Then a Gaussian mixture-based approach is proposed to calculate the voting weight of each group. Finally, a recommendation list of the target user is achieved by comprehensively considering suggestions of each group according to the corresponding voting weight. As compared to the state-of-the-art POI recommendation methods, the experimental results demonstrate that our method exhibits much better performance.

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Acknowledgment

This work is supported by Natural Science Foundation of Tianjin, China (18JCQNJC00700) and Tianjin “Project + Team” Key Training Project under Grant No. XC202022.

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  1. Department of Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Shuang Liu, Leilei Yang, Wenguang Zheng, Yingyuan Xiao & Li Liu

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  1. Shuang Liu
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  2. Leilei Yang
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  3. Wenguang Zheng
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  4. Yingyuan Xiao
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  5. Li Liu
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Correspondence to Wenguang Zheng.

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Shuang Liu and Leilei Yang are contributed equally to this work.

This article belongs to the Topical Collection: Big Data-Driven Large-Scale Group Decision Making Under Uncertainty

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Liu, S., Yang, L., Zheng, W. et al. An ensemble learning model for preference-geographical aware point-of interest recommendation. Appl Intell 52, 13763–13780 (2022). https://doi.org/10.1007/s10489-022-04035-9

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