Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T., Wen, J.: Planning unobstructed paths in traffic-aware spatial networks. 23(3), 449–468 (2014)īao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. Shang, S., Ding, R., Zheng, K., Jensen, C., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities. Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: A random walk around the city: new venue recommendation in location-based social networks. Shang, S., Zheng, K., Jensen, C., Yang, B., Kalnis, B., Li, G., Wen, J.: Discovery of path nearby clusters in spatial networks. 1837–1843 (2017)Ĭhen, X., Zeng, Y., Cong, G., Qin, S., Xiang, Y., Dai, Y.: On information coverage for location category based point-of-interest recommendation. He, J., Li, X., Liao, L.: Category-aware next point-of-interest recommendation via listwise Bayesian personalized ranking. Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation. Shang, S., Lu, H., Pedersen, T., Xie, X.: Modeling of traffic-aware travel time in spatial networks. Zhang, J., Chow, C.: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. TKDE 28(5), 1132–1146 (2016)Ĭheng, C., Yang, H., King, I., Lyu, M.: Fused matrix factorization with geographical and social influence in location-based social networks. Shang, S., Chen, L., Wei, Z., Jensen, C., Wen, J., Kalnis, P.: Collective travel planning in spatial networks. Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.: Adapting to user interest drift for POI recommendation. Shang, S., Chen, L., Jensen, C., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. Hosseini, S., Yin, H., Zhang, M., Zhou, X., Sadiq, S.: Jointly modeling heterogeneous temporal properties in location recommendation. Zhang, J., Chow, C.: GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. Li, X., Cong, G., Li, X., Pham, T., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. It outperforms state-of-the-art models for the long tail POI recommendation problem. Experimental results based on two public datasets demonstrate that our model is effective and competitive. To this end, this paper proposes a new model, named GRM (geographical relevance model), that expands POI profiles via relevant POIs and employs the geographical information, addressing the limitations of existing models. It is interesting and meaningful to investigate the long tail POI recommendation from the POI perspective. In this paper, we observe that the “long tail” POIs, which have few check-ins and have less opportunity to be exposed, take up a great proportion among all the POIs. To the best of our knowledge, few attention has been made to address this issue from the POI perspective. Existing works have proposed various models to alleviate the bottleneck of the data sparsity, and most of these works addressed this issue from the user perspective. One of important phenomena in the POI recommendation community is the data sparsity, which makes deep impact on the quality of recommendation. Point-of-Interest (POI) recommendation plays a key role in people’s daily life, and has been widely studied in recent years, due to its increasingly applications (e.g., recommending new restaurants for users).
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