Abstract
With the increasing popularity of location-based social networks (LBSNs), an increasing number of people are sharing their locations with friends through check-in activities. Point-of-interest (POI) recommendation, in which new places are suggested to users, is one of the most important tasks in LBSNs. POI group recommendation aims to suggest the most agreeable places for a group of users. However, the most existing studies, based on collaborative filtering, suffered from various issues, such as: (1) data sparseness, (2) cold start, and (3) scalability. Moreover, many existing schemes are limited in functionality. To address the aforementioned issues, we proposes a POI group recommendation model from a new perspective. The model combines machine learning knowledge and considers POI group recommendation as a classification problem. Extreme learning machine (ELM) is used to train the features. ELM has fast learning speed and ensures the recommendation efficiency. Finally, a series of experiments verify the performance of the model with ELM.
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Acknowledgment
This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 61672145, 61572121, 61602323, 61702086, and U1401256.
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The authors declare that they have no potential conflict of interest. This article does not contain any studies involving human participants and/or animals by any of the authors. Informed consent was obtained from all individual participants.
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Zhang, Z., Wang, G., Zhao, X. (2020). Point-of-Interest Group Recommendation with an Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_14
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DOI: https://doi.org/10.1007/978-3-030-23307-5_14
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