Abstract
The development of location-based social networks (LBSNs) generates large volume of check-in data. Point-of-interest recommendation (POI) is important for users to find some attractive venues, sometimes when users are in some places far away from their living cities. However, POI recommendation is so difficult compared to the classical recommender system. Users may access only a small portion of POIs, with a sparse user-POI matrix. The bulk of the POIs accessed by users located in a near close to users’ residences, which leads it difficult to put in a good word for POIs when the user travels to a faraway region. Meanwhile, uses’ preferences may be different in various geographical regions. Different users may prefer to go to different venues at different time. From our paper, we present a novel model represented as probabilistic graphical model to describe users’ check-in behaviors, which can overcome the data sparsity for the users far away from their living cities. To demonstrate our proposed model can recommend effectively, we do experiments to calculate the precision. The results show our model can do recommendation effectively and efficiently.
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Guo, Z., Changyi, M. (2018). Personalized POI Recommendation Model in LBSNs. In: Xhafa, F., Patnaik, S., Zomaya, A. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2017. Advances in Intelligent Systems and Computing, vol 686. Springer, Cham. https://doi.org/10.1007/978-3-319-69096-4_85
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DOI: https://doi.org/10.1007/978-3-319-69096-4_85
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