{"type":"ScholarlyArticle","title":"Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation","authors":[{"name":"Xiaolin Wang","slug":"xiaolin-wang","given":"Xiaolin","family":"Wang","affiliation":"Donghua University; Shanghai AI Laboratory"},{"name":"Guohao Sun","slug":"guohao-sun","given":"Guohao","family":"Sun","affiliation":"Donghua University"},{"name":"Xiu Fang","slug":"xiu-fang","given":"Xiu","family":"Fang","affiliation":"Donghua University"},{"name":"Jian Yang","slug":"jian-yang","given":"Jian","family":"Yang","affiliation":"Macquarie University , School of Computing"},{"name":"Shoujin Wang","slug":"shoujin-wang","given":"Shoujin","family":"Wang","affiliation":"RMIT University"}],"abstract":"Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timestamps. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timestamp. And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.","doi":"10.24963/ijcai.2022/490","url":"https://ijcai.org/proceedings/2022/490","pdf":"https://ijcai.org/proceedings/2022/0490.pdf","markdown":"https://ijcai.org/proceedings/2022/490.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/490","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":490,"pages":{"first":3530,"last":3536},"keywords":["Data Mining: Knowledge Graphs and Knowledge Base Completion","Machine Learning: Recommender Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986685","length_sec":76,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984828","length_sec":305,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Cheng et al., 2013] Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. Where you like to go next: Suc- cessive point-of-interest recommendation. In IJCAI, page 2605-2611, 2013.","title":"Where you like to go next: Successive point-of-interest recommendation","authors":"Chen Cheng, Haiqin Yang, Michael R Lyu, Irwin King","year":"2013","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2013/384"},{"position":2,"raw":"[Cho et al., 2011] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In SIGKDD, page 1082-1090, 2011.","title":"Friendship and mobility: User movement in location-based social networks","authors":"Cho","year":"2011","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chung et al., 2014] Junyoung Chung, C ¸aglar Gülc ¸ehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evalua- tion of gated recurrent neural networks on sequence mod- eling. CoRR, abs/1412.3555, 2014.","title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","authors":"Chung","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Feng et al., 2015] Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. Person- alized ranking metric embedding for next new poi recom- mendation. In ICOAI, page 2069-2075, 2015.","title":"Personalized ranking metric embedding for next new poi recommendation","authors":"Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, Quan Yuan","year":"2015","venue":"ICOAI","doi":null,"url":null,"internal_url":"/proceedings/2015/293"},{"position":5,"raw":"[Graves, 2008] Alex Graves. Supervised sequence labelling with recurrent neural networks. Studies in Computational Intelligence, 2008.","title":"Supervised sequence labelling with recurrent neural networks","authors":"Alex Graves","year":"2008","venue":"Studies in Computational Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, 11 1997.","title":"Long Short-Term Memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kang and McAuley, 2018] Wang-Cheng Kang and Julian McAuley. Self-attentive sequential recommendation. In Proceedings of the ACM IEEE International Conference on Data Mining, pages 197-206, 2018.","title":"Self-attentive sequential recommendation","authors":"Wang-Cheng Kang, Julian Mcauley","year":"2018","venue":"Proceedings of the ACM IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2020] Jiacheng Li, Yujie Wang, and Julian McAuley. Time interval aware self-attention for sequen- tial recommendation. In WSDM, page 322-330, 2020.","title":"Time interval aware self-attention for sequential recommendation","authors":"Li","year":"2020","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lian et al., 2020] Defu Lian, Yongji Wu, Yong Ge, Xing Xie, and Enhong Chen. Geography-aware sequential lo- cation recommendation. In SIGKDD, page 2009-2019, 2020.","title":"Geography-aware sequential location recommendation","authors":"Defu Lian, Yongji Wu, Yong Ge, Xing Xie, Enhong Chen","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Liu et al., 2016] Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI, page 194-200, 2016.","title":"Predicting the next location: A recurrent model with spatial and temporal contexts","authors":"Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Luo et al., 2021] Yingtao Luo, Qiang Liu, and Zhaocheng Liu. Stan: Spatio-temporal attention network for next lo- cation recommendation. In WWW, page 2177-2185, 2021.","title":"Stan: Spatio-temporal attention network for next location recommendation","authors":"Luo","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, page 452-461, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Schlichtkrull et al., 2018] Michael Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convo- lutional networks. CoRR, abs/1703.06103, 2018.","title":"Modeling relational data with graph convolutional networks","authors":"Michael Schlichtkrull, Thomas Kipf, Peter Bloem, Rianne Van Den, Ivan Berg, Max Titov, Welling","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Schuster and Paliwal, 1997] Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673-2681, 1997.","title":"Bidirectional recurrent neural networks","authors":"Mike Schuster, Kuldip K Paliwal","year":"1997","venue":"IEEE Transactions on Signal Processing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Tang and Wang, 2018] Jiaxi Tang and Ke Wang. Person- alized top-n sequential recommendation via convolutional sequence embedding. In WSDM, page 565-573, 2018.","title":"Personalized top-n sequential recommendation via convolutional sequence embedding","authors":"Wang ; ] Jiaxi Tang, Ke Tang, Wang","year":"2018","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NIPS, page 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł Ukasz Kaiser, Illia Polosukhin","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2018a] Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In CIKM, page 417-426, 2018.","title":"Ripplenet: Propagating user preferences on the knowledge graph for recommender systems","authors":"Wang","year":"2018","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wang et al., 2018b] Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. Attention- based transactional context embedding for next-item rec- ommendation. In AAAI, pages 2532-2539, 2018.","title":"Attentionbased transactional context embedding for next-item recommendation","authors":"Wang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2019a] Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. Knowledge graph convolu- tional networks for recommender systems. In WWW, page 3307-3313, 2019.","title":"Knowledge graph convolutional networks for recommender systems","authors":"Wang","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2019b] Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. Kgat: Knowledge graph attention network for recommendation. In SIGKDD, page 950-958, 2019.","title":"Kgat: Knowledge graph attention network for recommendation","authors":"Wang","year":"2019","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2021] Shoujin Wang, Liang Hu, Yan Wang, Xi- angnan He, Quan Z Sheng, Mehmet A Orgun, Longbing Cao, Francesco Ricci, and Philip S Yu. Graph learning based recommender systems: A review. In IJCAI, pages 4644-4652, 2021.","title":"Graph learning based recommender systems: A review","authors":"Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Z Quan, Sheng, Longbing Mehmet A Orgun, Francesco Cao, Philip S Ricci, Yu","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/630"},{"position":22,"raw":"[Wu et al., 2020] Yuxia Wu, Ke Li, Guoshuai Zhao, and Xueming QIAN. Personalized long-and short-term pref- erence learning for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2020.3002531, 2020.","title":"Personalized long-and short-term preference learning for next poi recommendation","authors":"Yuxia Wu, Ke Li, Guoshuai Zhao, Xueming Qian","year":"2020","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":"10.1109/tkde.2020.3002531","url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2015] Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. Modeling user activity prefer- ence by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernet- ics: Systems, 45(1):129-142, 2015.","title":"Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns","authors":"Yang","year":"2015","venue":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yao et al., 2017] Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi. Serm: A recurrent model for next loca- tion prediction in semantic trajectories. In CIKM, page 2411-2414, 2017.","title":"Serm: A recurrent model for next location prediction in semantic trajectories","authors":"Di Yao, Chao Zhang, Jianhui Huang, Jingping Bi","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhou and Huang, 2018] Yang Zhou and Yan Huang. Deep- move: Learning place representations through large scale movement data. In IEEE BigData, pages 2403-2412, 2018.","title":"Deepmove: Learning place representations through large scale movement data","authors":"Yang Zhou, Yan Huang","year":"2018","venue":"IEEE BigData","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhu et al., 2021] Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhan. Learning from his- tory: Modeling temporal knowledge graphs with sequen- tial copy-generation networks. In AAAI, pages 4732-4740, 2021.","title":"Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks","authors":"Zhu","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}