Spatiotemporal Representation Learning for Translation-Based POI Recommendation

Abstract

The increasing proliferation of location-based social networks brings about a huge volume of user check-in data, which facilitates the recommendation of points of interest (POIs). Time and location are the two most important contextual factors in the user’s decision-making for choosing a POI to visit. In this article, we focus on the spatiotemporal context-aware POI recommendation, which considers the joint effect of time and location for POI recommendation. Inspired by the recent advances in knowledge graph embedding, we propose a spatiotemporal context-aware and translation-based recommender framework (STA) to model the third-order relationship among users, POIs, and spatiotemporal contexts for large-scale POI recommendation. Specifically, we embed both users and POIs into a “transition space” where spatiotemporal contexts (i.e., a <time, location> pair) are modeled as translation vectors operating on users and POIs. We further develop a series of strategies to exploit various correlation information to address the data sparsity and cold-start issues for new spatiotemporal contexts, new users, and new POIs. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate that our STA framework achieves the superior performance in terms of high recommendation accuracy, robustness to data sparsity, and effectiveness in handling the cold-start problem.

References

1. Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alex Tuzhilin. 2011. Context-aware recommender systems. AI MAGAZINE Fall (2011), 67--80.
2. Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014. A semantic matching energy function for learning with multi-relational data. Machine Learning 94, 2 (2014), 233--259.
3. Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, and Jaewoo Kang. 2018. Content-aware hierarchical point-of-interest embedding model for successive POI recommendation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18). 3301--3307.
4. Xuefeng Chen, Yifeng Zeng, Gao Cong, Shengchao Qin, Yanping Xiang, and Yuanshun Dai. 2015. On information coverage for location category based point-of-interest recommendation. In Proceedings of AAAI Conference on Artificial Intelligence. 37--43.
5. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of AAAI Conference on Artificial Intelligence. 17--23.
6. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2016. A unified point-of-interest recommendation framework in location-based social networks. ACM TIST 8, 1 (10 2016), 1--21.
7. Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of International Joint Conference on Artificial Intelligence. 2605--2611.
8. Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1082--1090.
9. Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of 24th International Joint Conference on Artificial Intelligence. 2069--2075.
10. Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2015b. Content-aware point of interest recommendation on location-based social networks. In Proceedings of 29th AAAI Conference on Artificial Intelligence. 1721--1727.
11. Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In ACM Conference on Recommender Systems. 93--100.
12. Huiji Gao, Jiliang Tang, and Huan Liu. 2012. gSCorr: Modeling geo-social correlations for new check-ins on location-based social networks. In Proceedings of ACM International Conference on Information and Knowledge Management. 1582--1586.
13. Huiji Gao, Jiliang Tang, and Huan Liu. 2015a. Addressing the cold-start problem in location recommendation using geo-social correlations. Data Mining and Knowledge Discovery 29, 2 (2015), 299--323.
14. X. Glorot and Y. Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). 249--256.
15. Jean-Benoit Griesner, Talel Abdessalem, and Hubert Naacke. 2015. POI recommendation: Towards fused matrix factorization with geographical and temporal influences. In Proceedings of the 9th ACM Conference on Recommender Systems. 301--304.
16. Mengyue Hang, Ian Pytlarz, and Jennifer Neville. 2018. Exploring student check-in behavior for improved point-of-interest prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 321--330.
17. Ruining He, Wang Cheng Kang, and Julian Mcauley. 2017a. Translation-based recommendation. In ACM Conference on Recommender Systems. 161--169.
18. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. 173--182.
19. Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In Proceedings of the 26th International Conference on World Wide Web. 193--201.
20. Bo Hu and Martin Ester. 2014. Social topic modeling for point-of-interest recommendation in location-based social networks. In Proceedings of IEEE International Conference on Data Mining. 845--850.
21. Huayu Li, Yong Ge, Richang Hong, and Hengshu Zhu. 2016. Point-of-interest recommendations: Learning potential check-ins from friends. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 975--984.
22. Xutao Li, Gao Cong, Xiaoli Li, Tuan Anh Nguyen Pham, and Shonali Krishnaswamy. 2015. Rankgeofm: A ranking based geographical factorization method for point of interest recommendation. In Proceedings of International Conference on Research on Development in Information Retrieval. 433--442.
23. Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 831--840.
24. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of 29th AAAI Conference on Artificial Intelligence. 2181--2187.
25. Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013a. Learning geographical preferences for point-of-interest recommendation. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1043--1051.
26. Bin Liu and Hui Xiong. 2013. Point-of-interest recommendation in location based social networks with topic and location awareness. In Proceedings of the 2013 SIAM International Conference on Data Mining (SDM). 396--404.
27. Qi Liu, Enhong Chen, Hui Xiong, Yong Ge, Zhongmou Li, and Xiang Wu. 2014a. A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering (TKDE) 26, 2 (2014), 278--293.
28. Qi Liu, Yong Ge, Zhongmou Li, Enhong Chen, and Hui Xiong. 2011. Personalized travel package recommendation. In Proceedings of the 11th IEEE International Conference on Data Mining. 407--416.
29. Qi Liu, Haiping Ma, Enhong Chen, and Hui Xiong. 2013b. A survey of context-aware mobile recommendations. International Journal of Information Technology and Decision Making (IJITDM) 12, 1 (2013), 139--172.
30. Xin Liu, Yong Liu, and Xiaoli Li. 2016. Exploring the context of locations for personalized location recommendations. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 1188--1194.
31. Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014b. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of ACM International Conference on Information and Knowledge Management. 739--748.
32. Shaojie Qiao, Nan Han, Jiliu Zhou, Rong-Hua Li, Cheqing Jin, and Louis Alberto Gutierrez. 2018. SocialMix: A familiarity-based and preference-aware location suggestion approach. Engineering Applications of Artificial Intelligence 68 (2018), 192--204.
33. Salvatore Scellato, Anastasios Noulas, Renaud Lambiotte, and Cecilia Mascolo. 2011. Socio-spatial properties of online location-based social networks.. In Proceedings of International Conference on Web and Social Media (ICWSM). 329--336.
34. Hao Wang, Huawei Shen, Wentao Ouyang, and Xueqi Cheng. 2018. Exploiting POI-specific geographical influence for point-of-interest recommendation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18). 3877--3883.
35. Hao Wang, Naiyan Wang, and Dit Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1235--1244.
36. Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Wasim Sadiq, and Xiaofang Zhou. 2017. ST-SAGE: A spatial-temporal sparse additive generative model for spatial item recommendation.ACM TIST 8, 48 (3 2017), 1--25.
37. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of 28th AAAI Conference on Artificial Intelligence. 1112--1119.
38. Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016. Learning graph-based POI embedding for location-based recommendation. In Proceedings of ACM International Conference on Information and Knowledge Management. 15--24.
39. Mao Ye, Krzysztof Janowicz, and Wang Chien Lee. 2011a. What you are is when you are: The temporal dimension of feature types in location-based social networks. In Proceedings of ACM Sigspatial International Conference on Advances in Geographic Information Systems. 102--111.
40. Mao Ye, Peifeng Yin, and Wang Chien Lee. 2010. Location recommendation for location-based social networks. In Proceedings of ACM Sigspatial International Conference on Advances in Geographic Information Systems. 458--461.
41. Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011b. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of International Conference on Research on Development in Information Retrieval. 325--334.
42. Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia W. Sadiq. 2016a. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems 35, 2 (2016), 11:1--11:44.
43. Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, and Ling Chen. 2013. LCARS: A location-content-aware recommender system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 221--229.
44. Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, and Xiaofang Zhou. 2017. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering 29, 11 (2017), 2537--2551.
45. Hongzhi Yin, Xiaofang Zhou, Bin Cui, Hao Wang, Kai Zheng, and Nguyen Quoc Viet Hung. 2016b. Adapting to user interest drift for POI recommendation. IEEE Transactions on Knowledge and Data Engineering 28, 10 (2016), 2566--2581.
46. Hongzhi Yin, Xiaofang Zhou, Yingxia Shao, Hao Wang, and Shazia Sadiq. 2015. Joint modeling of user check-in behaviors for point-of-interest recommendation. In Proceedings of ACM International Conference on Information and Knowledge Management. 1631--1640.
47. Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat Thalmann. 2013. Time-aware point-of-interest recommendation. In Proceedings of International Conference on Research Development in Information Retrieval. 363--372.
48. Jia Dong Zhang, Yanhua Li, and Yanhua Li. 2014. LORE: Exploiting sequential influence for location recommendations. In ACM Sigspatial International Conference on Advances in Geographic Information Systems. 103--112.
49. Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R. Lyu, and Irwin King. 2016. STELLAR: Spatial-temporal latent ranking for successive point-of-interest recommendation. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 316--321.
50. Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International World Wide Web Conference. 791--800.
51. Wen-Yuan Zhu, Wen-Chih Peng, Ling-Jyh Chen, Kai Zheng, and Xiaofang Zhou.2015. Modeling user mobility for location promotion in location-based social networks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1573--1582.

Index Terms

• Information systems

  • World Wide Web

    • Web searching and information discovery

      • Social recommendation