{"type":"ScholarlyArticle","title":"An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins","authors":[{"name":"Lu Zhang","slug":"lu-zhang","given":"Lu","family":"Zhang","affiliation":"Nanyang Technological University , Singapore"},{"name":"Zhu Sun","slug":"zhu-sun","given":"Zhu","family":"Sun","affiliation":"Macquarie University , Australia"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Nanyang Technological University , Singapore"},{"name":"Yu Lei","slug":"yu-lei","given":"Yu","family":"Lei","affiliation":"Yanshan University , China"},{"name":"Chen Li","slug":"chen-li","given":"Chen","family":"Li","affiliation":"Yanshan University , China"},{"name":"Ziqing Wu","slug":"ziqing-wu","given":"Ziqing","family":"Wu","affiliation":"Nanyang Technological University , Singapore"},{"name":"Horst Kloeden","slug":"horst-kloeden","given":"Horst","family":"Kloeden","affiliation":"BMW Group , Germany"},{"name":"Felix Klanner","slug":"felix-klanner","given":"Felix","family":"Klanner","affiliation":"BMW Group , Germany"}],"abstract":"Studies on next point-of-interest (POI) recommendation mainly seek to learn users' transition patterns with certain historical check-ins. However, in reality, users' movements are typically uncertain (i.e., fuzzy and incomplete) where most existing methods suffer from the transition pattern vanishing issue. To ease this issue, we propose a novel interactive multi-task learning (iMTL) framework to better exploit the interplay between activity and location preference. Specifically, iMTL introduces: (1) temporal-aware activity encoder equipped with fuzzy characterization over uncertain check-ins to unveil the latent activity transition patterns; (2) spatial-aware location preference encoder to capture the latent location transition patterns; and (3) task-specific decoder to make use of the learned latent transition patterns and enhance both activity and location prediction tasks in an interactive manner. Extensive experiments on three real-world datasets show the superiority of iMTL.","doi":"10.24963/ijcai.2020/491","url":"https://ijcai.org/proceedings/2020/491","pdf":"https://ijcai.org/proceedings/2020/0491.pdf","markdown":"https://ijcai.org/proceedings/2020/491.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/491","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":491,"pages":{"first":3551,"last":3557},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Humans and AI: Personalization and User Modeling"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25084/file_25084.m3u8","length_sec":244,"provider":"hls"}],"references":[{"position":1,"raw":"[Caruana, 1997] Rich Caruana. Multitask learning. Machine learning, 28(1):41-75, 1997.","title":"Multitask learning","authors":"Caruana","year":"1997","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2019] Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen. Co-attentive multi-task learning for explainable recom- mendation. In IJCAI, pages 2137-2143, 2019.","title":"Co-attentive multi-task learning for explainable recommendation","authors":"Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, Enhong Chen","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/296"},{"position":3,"raw":"[Duchi et al., 2011] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. JMLR, 12(Jul):2121-2159, 2011.","title":"Adaptive subgradient methods for online learning and stochastic optimization","authors":"John Duchi, Elad Hazan, Yoram Singer","year":"2011","venue":"JMLR","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 IJCAI, pages 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":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/293"},{"position":5,"raw":"[Feng et al., 2018] Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. Deep- move: Predicting human mobility with attentional recur- rent networks. In WWW, pages 1459-1468, 2018.","title":"Deepmove: Predicting human mobility with attentional recurrent networks","authors":"Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin","year":"2018","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Girshick, 2015] Ross Girshick. Fast R-CNN. In CVPR, pages 1440-1448, 2015.","title":"Fast R-CNN","authors":"Ross Girshick","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[He et al., 2017] Jing He, Xin Li, and Lejian Liao. Category- aware next point-of-interest recommendation via listwise bayesian personalized ranking. In IJCAI, pages 1837- 1843, 2017.","title":"Categoryaware next point-of-interest recommendation via listwise bayesian personalized ranking","authors":"Jing He, Xin Li, Lejian Liao","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 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":9,"raw":"[Huang et al., 2019] Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. An attention-based spatiotemporal lstm network for next poi recommendation. IEEE Transactions on Services Computing, 2019.","title":"An attention-based spatiotemporal lstm network for next poi recommendation","authors":"Liwei Huang, Yutao Ma, Shibo Wang, Yanbo Liu","year":"2019","venue":"IEEE Transactions on Services Computing","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2018] Ming Li, Rene Westerholt, Hongchao Fan, and Alexander Zipf. Assessing spatiotemporal predictabil- ity of LBSN: A case study of three foursquare datasets. GeoInformatica, 22(3):541-561, 2018.","title":"Assessing spatiotemporal predictability of LBSN: A case study of three foursquare datasets","authors":"Ming Li, Rene Westerholt, Hongchao Fan, Alexander Zipf","year":"2018","venue":"GeoInformatica","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liao et al., 2017] Dongliang Liao, Yuan Zhong, and Jing Li. Location prediction through activity purpose: Integrating temporal and sequential models. In KDD, pages 711-723, 2017.","title":"Location prediction through activity purpose: Integrating temporal and sequential models","authors":"Dongliang Liao, Yuan Zhong, Jing Li","year":"2017","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liao et al., 2018] Dongliang Liao, Weiqing Liu, Yuan Zhong, Jing Li, and Guowei Wang. Predicting activity and location with multi-task context aware recurrent neu- ral network. In IJCAI, pages 3435-3441, 2018.","title":"Predicting activity and location with multi-task context aware recurrent neural network","authors":"Dongliang Liao, Weiqing Liu, Yuan Zhong, Jing Li, Guowei Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/477"},{"position":13,"raw":"[Liu et al., 2013] Xin Liu, Yong Liu, Karl Aberer, and Chun- yan Miao. Personalized point-of-interest recommendation by mining users' preference transition. In CIKM, pages 733-738, 2013.","title":"Personalized point-of-interest recommendation by mining users' preference transition","authors":"Xin Liu, Yong Liu, Karl Aberer, Chunyan Miao","year":"2013","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2016] Qiang Liu, Shu Wu, Liang Wang, and Tie- niu Tan. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI, pages 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":15,"raw":"[Lu et al., 2018] Yichao Lu, Ruihai Dong, and Barry Smyth. Why I like it: multi-task learning for recommendation and explanation. In Recsys, pages 4-12, 2018.","title":"Why I like it: multi-task learning for recommendation and explanation","authors":"Lu","year":"2018","venue":"Recsys","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed rep- resentations of words and phrases and their composition- ality. In NIPS, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"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, pages 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":18,"raw":"[Sengstock et al., 2013] Christian Sengstock, Michael Gertz, Florian Flatow, and Hamed Abdelhaq. A probablistic model for spatio-temporal signal extraction from social media. In SIGSPATIAL, pages 274-283, 2013.","title":"A probablistic model for spatio-temporal signal extraction from social media","authors":"Sengstock","year":"2013","venue":"SIGSPATIAL","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sun et al., 2019] Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, and Robin Burke. Research com- mentary on recommendations with side information: A survey and research directions. ECRA, 37:100879, 2019.","title":"Research commentary on recommendations with side information: A survey and research directions","authors":"Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke","year":"2019","venue":"ECRA","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Yang et al., 2016] Dingqi Yang, Daqing Zhang, and Bingqing Qu. Participatory cultural mapping based on collective behavior data in location-based social networks. ACM (TIST), 7(3):30, 2016.","title":"Participatory cultural mapping based on collective behavior data in location-based social networks","authors":"Yang","year":"2016","venue":"ACM (TIST)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yao et al., 2017] Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi. Serm: A recurrent model for next location prediction in semantic trajectories. In CIKM, pages 2411- 2414. ACM, 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":22,"raw":"[Ye et al., 2013] Jihang Ye, Zhe Zhu, and Hong Cheng. What's your next move: User activity prediction in location-based social networks. In ICDM, pages 171-179, 2013.","title":"What's your next move: User activity prediction in location-based social networks","authors":"Ye","year":"2013","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhang et al., 2019] Lu Zhang, Zhu Sun, Jie Zhang, Horst Kloeden, and Felix Klanner. Modeling hierarchical cate- gory transition for next poi recommendation with uncer- tain check-ins. Information Sciences, 2019.","title":"Modeling hierarchical category transition for next poi recommendation with uncertain check-ins","authors":"Lu Zhang, Zhu Sun, Jie Zhang, Horst Kloeden, Felix Klanner","year":"2019","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhao et al., 2016] Shenglin Zhao, Tong Zhao, Haiqin Yang, Michael R Lyu, and Irwin King. Stellar: spatial-temporal latent ranking for successive point-of-interest recommen- dation. In AAAI, pages 315-321, 2016.","title":"Stellar: spatial-temporal latent ranking for successive point-of-interest recommendation","authors":"Zhao","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhao et al., 2017] Shenglin Zhao, Tong Zhao, Irwin King, and Michael R Lyu. Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation. In WWW, pages 153-162, 2017.","title":"Geo-teaser: geo-temporal sequential embedding rank for point-of-interest recommendation","authors":"Zhao","year":"2017","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhao et al., 2019a] Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S Sheng, and Xiaofang Zhou. Where to go next: a spatio-temporal gated network for next poi recommendation. In AAAI, pages 5877-5884, 2019.","title":"Where to go next: a spatio-temporal gated network for next poi recommendation","authors":"Zhao","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhao et al., 2019b] Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. Recommending what video to watch next: a multi- task ranking system. In RecSys, pages 43-51. ACM, 2019.","title":"Recommending what video to watch next: a multitask ranking system","authors":"Zhao","year":"2019","venue":"RecSys","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}