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CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users

Published: 07 July 2022 Publication History

Abstract

Pre-travel out-of-town recommendation aims to recommend Point-of-Interests (POIs) to the users who plan to travel out of their hometown in the near future yet have not decided where to go, i.e., their destination regions and POIs both remain unknown. It is a non-trivial task since the searching space is vast, which may lead to distinct travel experiences in different out-of-town regions and eventually confuse decision-making. Besides, users' out-of-town travel behaviors are affected not only by their personalized preferences but heavily by others' travel behaviors. To this end, we propose a Crowd-Aware Pre-Travel Out-of-town Recommendation framework (CAPTOR) consisting of two major modules: spatial-affined conditional random field (SA-CRF) and crowd behavior memory network (CBMN). Specifically, SA-CRF captures the spatial affinity among POIs while preserving the inherent information of POIs. Then, CBMN is proposed to maintain the crowd travel behaviors w.r.t. each region through three affiliated blocks reading and writing the memory adaptively. We devise the elaborated metric space with a dynamic mapping mechanism, where the users and POIs are distinguishable both inherently and geographically. Extensive experiments on two real-world nationwide datasets validate the effectiveness of CAPTOR against the pre-travel out-of-town recommendation task.

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References

[1]
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, Vol. 26 (2013).
[2]
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 IJCAI. 3301--3307.
[3]
Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Twenty-Third international joint conference on Artificial Intelligence .
[4]
Jingtao Ding, Guanghui Yu, Yong Li, Depeng Jin, and Hui Gao. 2019. Learning from hometown and current city: Cross-city POI recommendation via interest drift and transfer learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 3, 4 (2019), 1--28.
[5]
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 world wide web conference. 1459--1468.
[6]
Gregory Ference, Mao Ye, and Wang-Chien Lee. 2013. Location recommendation for out-of-town users in location-based social networks. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 721--726.
[7]
Hongchang Gao, Jian Pei, and Heng Huang. 2019. Conditional random field enhanced graph convolutional neural networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 276--284.
[8]
Dan Guo, Yang Wang, Peipei Song, and Meng Wang. 2020. Recurrent relational memory network for unsupervised image captioning. arXiv preprint arXiv:2006.13611 (2020).
[9]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.
[10]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).
[11]
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.
[12]
Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . 687--696.
[13]
Linlang Jiang, Jingbo Zhou, Tong Xu, Yanyan Li, Hao Chen, Jizhou Huang, and Hui Xiong. 2021. Adversarial Neural Trip Recommendation. arXiv preprint arXiv:2109.11731 (2021).
[14]
Wenxiang Jiao, Michael Lyu, and Irwin King. 2020. Real-time emotion recognition via attention gated hierarchical memory network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 8002--8009.
[15]
SeongKu Kang, Junyoung Hwang, Dongha Lee, and Hwanjo Yu. 2019. Semi-supervised learning for cross-domain recommendation to cold-start users. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1563--1572.
[16]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.
[17]
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher. 2016. Ask me anything: Dynamic memory networks for natural language processing. In International conference on machine learning. PMLR, 1378--1387.
[18]
John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. (2001).
[19]
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. 2019. Multi-interest network with dynamic routing for recommendation at Tmall. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management . 2615--2623.
[20]
Dichao Li and Zhiguo Gong. 2020. A Deep Neural Network for Crossing-City POI Recommendations. IEEE Transactions on Knowledge and Data Engineering (2020).
[21]
Huayu Li, Yong Ge, Richang Hong, and Hengshu Zhu. 2016. Point-of-interest recommendations: Learning potential check-ins from friends. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 975--984.
[22]
Shuangli Li, Jingbo Zhou, Tong Xu, Hao Liu, Xinjiang Lu, and Hui Xiong. 2020. Competitive analysis for points of interest. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1265--1274.
[23]
Defu Lian, Yongji Wu, Yong Ge, Xing Xie, and Enhong Chen. 2020. Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 2009--2019.
[24]
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 the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 831--840.
[25]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Twenty-ninth AAAI conference on artificial intelligence .
[26]
Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In Thirtieth AAAI conference on artificial intelligence .
[27]
Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management . 739--748.
[28]
Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. In Proceedings of the Web Conference 2021. 2177--2185.
[29]
Tengfei Ma, Cao Xiao, Junyuan Shang, and Jimeng Sun. 2018. CGNF: Conditional Graph Neural Fields. (2018).
[30]
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell. 2017. Neural episodic control. In International Conference on Machine Learning . PMLR, 2827--2836.
[31]
Tieyun Qian, Bei Liu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2019. Spatiotemporal representation learning for translation-based POI recommendation. ACM Transactions on Information Systems (TOIS), Vol. 37, 2 (2019), 1--24.
[32]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[33]
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. 2015. End-to-end memory networks. arXiv preprint arXiv:1503.08895 (2015).
[34]
Huimin Sun, Jiajie Xu, Rui Zhou, Wei Chen, Lei Zhao, and Chengfei Liu. 2021. HOPE: a hybrid deep neural model for out-of-town next POI recommendation. World Wide Web (2021), 1--20.
[35]
Qiaoyu Tan, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, Xia Hu, et almbox. 2021. Dynamic memory based attention network for sequential recommendation. arXiv preprint arXiv:2102.09269 (2021).
[36]
Duyu Tang, Bing Qin, and Ting Liu. 2016. Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900 (2016).
[37]
Md Mehrab Tanjim, Congzhe Su, Ethan Benjamin, Diane Hu, Liangjie Hong, and Julian McAuley. 2020. Attentive sequential models of latent intent for next item recommendation. In Proceedings of The Web Conference 2020. 2528--2534.
[38]
Waldo R Tobler. 1970. A computer movie simulating urban growth in the Detroit region. Economic geography, Vol. 46, sup1 (1970), 234--240.
[39]
Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, and Hui Xiong. 2017a. A location-sentiment-aware recommender system for both home-town and out-of-town users. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 1135--1143.
[40]
Hao Wang, Huawei Shen, Wentao Ouyang, and Xueqi Cheng. 2018. Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation. In IJCAI . 3877--3883.
[41]
Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu. 2017b. What your images reveal: Exploiting visual contents for point-of-interest recommendation. In Proceedings of the 26th international conference on world wide web. 391--400.
[42]
Weiqing Wang, Hongzhi Yin, Ling Chen, Yizhou Sun, Shazia Sadiq, and Xiaofang Zhou. 2017c. ST-SAGE: A spatial-temporal sparse additive generative model for spatial item recommendation. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 8, 3 (2017), 1--25.
[43]
Markus Weimer, Alexandros Karatzoglou, Quoc Le, and Alex Smola. 2007. Cofirank-maximum margin matrix factorization for collaborative ranking. In Advances in Neural Information Processing Systems, 21st Annual Conference on Neural Information Processing Systems 2007. 222--230.
[44]
Jason Weston, Sumit Chopra, and Antoine Bordes. 2014. Memory networks. arXiv preprint arXiv:1410.3916 (2014).
[45]
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 346--353.
[46]
Guo-Sen Xie, Huan Xiong, Jie Liu, Yazhou Yao, and Ling Shao. 2021. Few-shot semantic segmentation with cyclic memory network. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7293--7302.
[47]
Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, and Hui Xiong. 2021. Out-of-Town Recommendation with Travel Intention Modeling. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4529--4536.
[48]
Bingbing Xu, Huawei Shen, Bingjie Sun, Rong An, Qi Cao, and Xueqi Cheng. 2021. Towards Consumer Loan Fraud Detection: Graph Neural Networks with Role-Constrained Conditional Random Field. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4537--4545.
[49]
Dingqi Yang, Daqing Zhang, Longbiao Chen, and Bingqing Qu. 2015. Nationtelescope: Monitoring and visualizing large-scale collective behavior in lbsns. Journal of Network and Computer Applications, Vol. 55 (2015), 170--180.
[50]
Dingqi Yang, Daqing Zhang, and Bingqing Qu. 2016. Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 7, 3 (2016), 1--23.
[51]
Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. LCARS: A spatial item recommender system. ACM Transactions on Information Systems (TOIS), Vol. 32, 3 (2014), 1--37.
[52]
Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia Sadiq. 2016a. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems (TOIS), Vol. 35, 2 (2016), 1--44.
[53]
Hongzhi Yin, Xiaofang Zhou, Bin Cui, Hao Wang, Kai Zheng, and Quoc Viet Hung Nguyen. 2016b. Adapting to user interest drift for poi recommendation. IEEE Transactions on Knowledge and Data Engineering, Vol. 28, 10 (2016), 2566--2581.
[54]
Jia-Dong Zhang and Chi-Yin Chow. 2015. Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval . 443--452.
[55]
Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. 2014. Lore: Exploiting sequential influence for location recommendations. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 103--112.
[56]
Weijia Zhang, Hao Liu, Fan Wang, Tong Xu, Haoran Xin, Dejing Dou, and Hui Xiong. 2021. Intelligent electric vehicle charging recommendation based on multi-agent reinforcement learning. In Proceedings of the Web Conference 2021 . 1856--1867.
[57]
Pengpeng Zhao, Anjing Luo, Yanchi Liu, Fuzhen Zhuang, Jiajie Xu, Zhixu Li, Victor S Sheng, and Xiaofang Zhou. 2020. Where to go next: A spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering (2020).
[58]
Pengpeng Zhao, Chengfeng Xu, Yanchi Liu, Victor S Sheng, Kai Zheng, Hui Xiong, and Xiaofang Zhou. 2019. Photo2Trip: Exploiting visual contents in geo-tagged photos for personalized tour recommendation. IEEE Transactions on Knowledge and Data Engineering (2019).
[59]
Xiao Zhou, Cecilia Mascolo, and Zhongxiang Zhao. 2019. Topic-enhanced memory networks for personalised point-of-interest recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3018--3028.
[60]
Nengjun Zhu, Jian Cao, Xinjiang Lu, and Hui Xiong. 2021. Learning a Hierarchical Intent Model for Next-Item Recommendation. ACM Transactions on Information Systems (TOIS), Vol. 40, 2 (2021), 1--28.

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
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Published: 07 July 2022

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Author Tags

  1. crf
  2. memory network
  3. metric learning
  4. pre-travel recommendation

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  • Natural Science Foundation of China
  • Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone
  • Shanghai Youth Science and Technology Talents Sailing Program

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Cited By

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  • (2024)Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation MethodElectronics10.3390/electronics1324495413:24(4954)Online publication date: 16-Dec-2024
  • (2024)KDDCProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/244(2207-2215)Online publication date: 3-Aug-2024
  • (2024)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 21-Nov-2024
  • (2024)CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature MatchingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679893(4148-4152)Online publication date: 21-Oct-2024
  • (2024)Learning place representations from spatial interactionsInternational Journal of Geographical Information Science10.1080/13658816.2024.233290838:6(1065-1090)Online publication date: 27-Mar-2024
  • (2023)Unvisited Out-Of-Town POI Recommendation with Simultaneous Learning of Multiple Regions2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386127(915-924)Online publication date: 15-Dec-2023

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