Abstract
With the rapid growth of the location-based social networks (LBSNs), Point of Interest (POI) recommendation has become an important research topic in data mining. However, the existing works do not reasonably utilize the time sensitivity of POI recommendations and have not taken full account of the user’s behavior preferences at different time, causing the POI recommendation performance poor. We propose a Time-aware and POI Recommendation model based on Tensor Factorization, named TPR-TF. Firstly, we study the POI recommendation problem of time sensitivity and propose a temporal dynamic segmentation algorithm based on hierarchical clustering. Through dividing the fine grain of time, the experiment result is more reasonable and effectively than the previous method which divided identical time empirically. Secondly, by combining the time-aware recommendation with the influence of the user’s direct friendship and potential friendship, we expand the scope of users’ social influence, and then further improve the POI recommendation performance. Experimental results on the two datasets indicate that our TPR-TF model is superior to the current mainstream POI recommendation models both in precision and recall.
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References
Guo, L., Wen, Y., Liu, F.: Location perspective-based neighborhood-aware POI recommendation in location-based social networks. Soft Comput. 23, 11935–11945 (2019). https://doi.org/10.1007/s00500-018-03748-9
He, J., Li, X., Liao, L., et al.: Next point-of-interest recommendation via a category-aware listwise Bayesian personalized ranking. J. Comput. Sci. 28, 206–216 (2018)
Huang, H., Dong, Y., Tang, J., et al.: Will triadic closure strengthen ties in social networks. ACM Trans. Knowl. Discov. Data 12(3), 1–25 (2018)
Shu-Dong, L., Xiang-Wu, M.: Recommender systems in location-based social networks. Chin. J. Comput. 38, 322–336 (2015)
Ye, M., Liu, X., Lee, W., et al.: Exploring social influence for recommendation: a generative model approach. In: International ACM SIGIR Conference on Research and Development in Information Retrieval 2012, pp. 671–680 (2012)
Gao, H., Tang, J., Hu, X., et al.: Exploring temporal effects for location recommendation on location-based social networks. In: Conference on Recommender Systems 2013, pp. 93–100 (2013)
Yuan, Q., Cong, G., Ma, Z., et al.: Time-aware point-of-interest recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval 2013, pp. 363–372 (2013)
Yuan, Q., Cong, G., Sun, A., et al.: Graph-based point-of-interest recommendation with geographical and temporal influences. In: Conference on Information and Knowledge Management 2014, pp. 659–668 (2014)
Zhang, W., Wang, J.: Location and time aware social collaborative retrieval for new successive point-of-interest recommendation. In: Conference on Information and Knowledge Management 2015, pp. 1221–1230 (2015)
Yao, Z., Fu, Y., Liu, B., et al.: POI recommendation: a temporal matching between poi popularity and user regularity. In: International Conference on Data Mining 2016, Barcelona, pp. 549–558 (2016)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Conference on Recommender Systems 2010, pp. 135–142 (2010)
Hao, M.A., Zhou, T.C., Lyu, M.R., et al.: Improving recommender systems by incorporating social contextual information. ACM Trans. Inf. Syst. 29(2), 77–100 (2011)
Bin, C., Gu, T., Sun, Y., Chang, L.: A personalized POI route recommendation system based on heterogeneous tourism data and sequential pattern mining. Multimed. Tools Appl. 78(24), 35135–35156 (2019). https://doi.org/10.1007/s11042-019-08096-w
Wu, L., Sun, P., Hong, R., et al.: SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation. https://arxiv.org/abs/1811.02815v2, July 2019
Guo, J., Zhang, W., Fan, W., et al.: Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. J. Manag. Inf. Syst. 35(4), 1121–1153 (2018)
Rendle, S., Freudenthaler, C., Gantner, Z., et al.: BPR: Bayesian personalized ranking from implicit feedback. In: Uncertainty in Artificial Intelligence 2009, Montreal, pp. 452–461 (2009)
Li, H., Diao, X., Cao, J., et al.: Tag-aware recommendation based on Bayesian personalized ranking and feature mapping. Intell. Data Anal. 23(3), 641–659 (2019)
Ma, H., Zhou, D., Liu, C., et al.: Recommender systems with social regularization. In: Web Search and Data Mining 2011, pp. 287–296 (2011)
Li, X., Cong, G., Li, X., et al.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval 2015, pp. 433–442 (2015)
Hosseini, S., Li, L.T.: Point-of-interest recommendation using temporal orientations of users and locations. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9642, pp. 330–347. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32025-0_21
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Wang, N., Liu, Y., Han, P., Li, X., Li, J. (2020). A New Personalized POI Recommendation Based on Time-Aware and Social Influence. In: Zhang, Q., Wang, Y., Zhang, LJ. (eds) Cloud Computing – CLOUD 2020. CLOUD 2020. Lecture Notes in Computer Science(), vol 12403. Springer, Cham. https://doi.org/10.1007/978-3-030-59635-4_14
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DOI: https://doi.org/10.1007/978-3-030-59635-4_14
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