Abstract
In the past few years, mobile application has been innovated by leaps and bounds, which leads the prevalence of location-based social networks (LBSNs). Point of interest (POI) recommendation aims to recommend satisfactory locations to users in mobile environment and plays an important role in LBSNs. However, there are still two challenges to be solved. One is the data sparseness caused by users who just visit a few POIs. The other is that it’s hard to make reasonable explanation of recommendation from the perspective of real world. Hence, firstly we propose a region-based collaborative filtering to alleviate the data sparseness by clustering locations into regions. Secondly, we model the impact of two kinds of user contexts like geographical distance and POI category to make POI recommendation more reasonable. Finally, we present a joint model called RCFC which combines the two parts mentioned above. Results of experiments on two real-world datasets demonstrate the model we propose outperforms the popular recommendation algorithms and is more in line with the situation in real world.
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References
Li, H., Ge, Y., Lian, D., Liu, H.: Learning user’s intrinsic and extrinsic interests for point-of-interest recommendation: a unified approach. In: The 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 2117–-2123 (2017)
Fletcher, K.K.: Regularizing matrix factorization with implicit user preference embeddings for web API recommendation. In: IEEE SCC, pp. 1-8 (2019)
Xue, H., Dai, X., Zhang, J., Huang, S., Chen, J.: Deep matrix factorization models for recommender systems. In: The 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 3203–-3209 (2017)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: ACM WWW, pp. 285–295 (2001)
Hu, S., Tu, Z., Wang, Z., Xu, X.: A POI-sensitive knowledge graph based service recommendation method. In: IEEE SCC, pp. 197–201 (2019)
Tran, T., Lee, K., Liao, Y., Lee, D.: Regularizing matrix factorization with user and item embeddings for recommendation. In: ACM CIKM, pp. 687–696 (2018)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: The 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (2016)
Li, H., Ge, Y., Hong, R., Zhu, H.: Point-of-interest recommendations: learning potential check-ins from friends. In: ACM KDD, pp. 975–984 (2016)
He, J., Li, X., Liao, L.: Category-aware next point-of-interest recommendation via listwise bayesian personalized ranking. In: The 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 1837–1843 (2017)
Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for POI recommendation. In: ACM KDD, pp. 1245–1254 (2017)
He, J., Li, X., Liao, L., Song, D., Cheung, W.K.: Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In: The 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 137–143 (2016)
Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: The 26th AAAI Conference on Artificial Intelligence, AAAI 2012, pp. 17–23 (2012)
Ye, M., Yin, P., Lee, W., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: ACM SIGIR, pp. 325–334 (2011)
Liu, Y., Wei, W., Sun, A., Miao, C.: Exploiting geographical neighborhood characteristics for location recommendation. In: ACM CIKM, pp. 739–748 (2014)
Liu, W., Wang, Z., Yao, B., Yin, J.: Geo-ALM: POI recommendation by fusing geographical information and adversarial learning mechanism. In: The 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 1807–1813 (2019)
Gao, H., Tang, J., Hu, X., Liu, H.: Content-aware point of interest recommendation on location-based social networks. In: The 29th AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 1721–1727 (2015)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidtthieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: The 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009, pp. 452–461 (2009)
Zeng, J., Tang, H., Li, Y., He, X.: A deep learning model based on sparse matrix for point-of-interest recommendation. In: The 31st International Conference on Software Engineering & Knowledge Engineering, SEKE 2019, pp. 379–492 (2019)
Zeng, J., Li, F., He, X., Wen, J.: Fused collaborative filtering with user preference, geographical and social influence for point of interest recommendation. Int. J. Web Serv. Res. (IJWSR) 16(4), 40–52 (2019)
Zeng, J., He, X., Tang, H., Wen, J.: A next location predicting approach based on a recurrent neural network and self-attention. In: The 15th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019, pp. 309–322 (2019)
Acknowledgements
This research is Sponsored by Natural Science Foundation of Chongqing, China (No.cstc2020jcyj-msxmX0900) and the Fundamental Research Funds for the Central Universities (Project No.2020CDJ-LHZZ-040).
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Zeng, J., Tang, H., He, X. (2021). RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_39
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