research-article

DAN-SNR: A Deep Attentive Network for Social-aware Next Point-of-interest Recommendation

Authors:

Keqing HeAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 1

Article No.: 2, Pages 1 - 27

https://doi.org/10.1145/3430504

Published: 22 December 2020 Publication History

Abstract

Next (or successive) point-of-interest (POI) recommendation, which aims to predict where users are likely to go next, has recently emerged as a new research focus of POI recommendation. Most of the previous studies on next POI recommendation attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, few of the next POI recommendation approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. We also carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely, Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is highly efficient among six neural-network-based methods, four of which utilize the attention mechanism.

References

[1]

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 the 26th AAAI Conference on Artificial Intelligence. AAAI Press, 17--23.

Digital Library

[2]

Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu. 2013. Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 93--100.

Digital Library

[3]

Liwei Huang, Yutao Ma, and Yanbo Liu. 2015. Point-of-interest recommendation in location-based social networks with personalized geo-social influence. China Commun. 12, 12 (2015), 21--31.

[4]

Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. PVLDB 10, 10 (2017), 1010--1021.

Digital Library

[5]

Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1082--1090.

Digital Library

[6]

Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015. Recommendations in location-based social networks: A survey. GeoInformatica 19, 3 (2015), 525--565.

Digital Library

[7]

V. S. Subrahmanian and Srijan Kumar. 2017. Predicting human behavior: The next frontiers. Science 355, 6324 (2017), 489.

[8]

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 the 23rd International Joint Conference on Artificial Intelligence. AAAI Press, 2605--2611.

Digital Library

[9]

Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Meng Yeow Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. AAAI Press, 2069--2075.

[10]

Shenglin Zhao, 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. AAAI Press, 315--322.

[11]

Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. AAAI Press, 194--200.

[12]

Lu Zhang, Zhu Sun, Jie Zhang, Horst Kloeden, and Felix Klanner. 2020. Modeling hierarchical category transition for next POI recommendation with uncertain check-ins. Inf. Sci. 515 (2020), 169--190.

Digital Library

[13]

Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. 2019. Where to go next: A spatio-temporal gated network for next POI recommendation. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. AAAI Press, 5877--5884.

Digital Library

[14]

Haochao Ying, Jian Wu, Guandong Xu, Yanchi Liu, Tingting Liang, Xiao Zhang, and Hui Xiong. 2019. Time-aware metric embedding with asymmetric projection for successive POI recommendation. World Wide Web 22, 5 (2019), 2209--2224.

Digital Library

[15]

Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. 2019. An attention-based spatiotemporal LSTM network for next POI recommendation. IEEE Trans. Serv. Comput. (2019), 1--1.

[16]

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 the 21st ACM International Conference on Information and Knowledge Management. ACM, 1582--1586.

Digital Library

[17]

Jia-Dong Zhang and Chi-Yin Chow. 2013. iGSLR: Personalized geo-social location recommendation: a kernel density estimation approach. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 334--343.

Digital Library

[18]

Liwei Huang, Yutao Ma, Yanbo Liu, and Arun Kumar Sangaiah. 2020. Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical-social networks. Fut. Gen. Comput. Syst. 108 (2020), 1119--1128.

[19]

Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, and Edward Y. Chang. 2017. A neural network approach to jointly modeling social networks and mobile trajectories. ACM Trans. Inf. Syst. 35, 4 (2017), 36:1--36:28.

Digital Library

[20]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 5998--6008.

[21]

Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, and Jun Gao. 2018. ATRank: An attention-based user behavior modeling framework for recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. AAAI Press, 4564--4571.

[22]

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. ACM, 103--112.

Digital Library

[23]

Jihang Ye, Zhe Zhu, and Hong Cheng. 2013. What's your next move: User activity prediction in location-based social networks. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 171--179.

[24]

Xin Li, Dongcheng Han, Jing He, Lejian Liao, and Mingzhong Wang. 2019. Next and next new POI recommendation via latent behavior pattern inference. ACM Trans. Inf. Syst. 37, 4 (2019), 46:1--46:28.

Digital Library

[25]

Laura Alessandretti, Piotr Sapiezynski, Vedran Sekara, Sune Lehmann, and Andrea Baronchelli. 2018. Evidence for a conserved quantity in human mobility. Nat. Hum. Behav. 2 (2018), 485--491.

[26]

Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, and Hui Xiong. 2016. Unified point-of-interest recommendation with temporal interval assessment. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1015--1024.

Digital Library

[27]

Jing He, Xin Li, Lejian Liao, Dandan Song, and William K. Cheung. 2016. Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. AAAI Press, 137--143.

[28]

Ranzhen Li, Yanyan Shen, and Yanmin Zhu. 2018. Next point-of-interest recommendation with temporal and multi-level context attention. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 1110--1115.

[29]

Yuxia Wu, Ke Li, Guoshuai Zhao, and Xueming Qian. 2019. Long-and short-term preference learning for next POI recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 2301--2304.

Digital Library

[30]

Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. 2014. Recurrent models of visual attention. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 2204--2212.

[31]

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 27th International Conference on World Wide Web. ACM, 1459--1468.

Digital Library

[32]

Qiang Gao, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Fengli Zhang. 2019. Predicting human mobility via variational attention. In Proceedings of the 28th International Conference on World Wide Web. ACM, 2750--2756.

Digital Library

[33]

Pavlos Kefalas, Panagiotis Symeonidis, and Yannis Manolopoulos. 2016. A graph-based taxonomy of recommendation algorithms and systems in LBSNs. IEEE Trans. Knowl. Data Eng. 28, 3 (2016), 604--622.

Digital Library

[34]

Huiji Gao, Jiliang Tang, and Huan Liu. 2012. Exploring social-historical ties on location-based social networks. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. AAAI Press, 114--121.

[35]

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. ACM, 975--984.

Digital Library

[36]

Josh Jia-Ching Ying, Wen-Ning Kuo, Vincent S. Tseng, and Eric Hsueh-Chan Lu. 2014. Mining user check-in behavior with a random walk for urban point-of-interest recommendations. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 40:1--40:26.

[37]

Pavlos Kefalas, Panagiotis Symeonidis, and Yannis Manolopoulos. 2018. Recommendations based on a heterogeneous spatio-temporal social network. World Wide Web 21, 2 (2018), 345--371.

Digital Library

[38]

Pavlos Kefalas and Yannis Manolopoulos. 2017. A time-aware spatio-textual recommender system. Expert Syst. Appl. 78, (2017), 396--406.

Digital Library

[39]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855--864.

Digital Library

[40]

Tao Xu, Yutao Ma, and Qian Wang. 2018. Cross-urban point-of-interest recommendation for non-natives. Int. J. Web Serv. Res. 15, 3 (2018), 82--102.

Digital Library

[41]

Bahdanau Dzmitry, Cho Kyunghyun, and Bengio Yoshua. 2014. Neural machine translation by jointly learning to align and translate. ArXiv.org, arXiv:1409.0473 (2014).

[42]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 770--778.

[43]

Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. arXiv.org, arXiv:1607.06450 (2016).

[44]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. Arxiv.Org, arXiv:1205.2618 (2012).

[45]

Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 843--852.

Digital Library

[46]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 3111--3119.

[47]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. ArXiv.org, arXiv:1412.6980 (2014).

[48]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM, 811--820.

Digital Library

[49]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. ArXiv.org, arXiv:1511.06939 (2015).

[50]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 197--206.

[51]

Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019. Session-based social recommendation via dynamic graph attention networks. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 555--563.

Digital Library

[52]

Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.

[53]

Chen Ting, Kornblith Simon, Norouzi Mohammad, and Hinton Geoffrey. 2020. A simple framework for contrastive learning of visual representations. arXiv.org, arXiv:2002.05709 (2020).

[54]

Hanhua Chen, Hai Jin, and Shaoliang Wu. 2016. Minimizing inter-server communications by exploiting self-similarity in online social networks. IEEE Trans. Parallel Distrib. Syst. 27, 4 (2016), 1116--1130.

Digital Library

Cited By

Chen JYe WKang S(2024)Learning user preferences from Multi-Contextual Sequence influences for next POI recommendationElectronic Research Archive10.3934/era.202402432:1(486-504)Online publication date: 2024
https://doi.org/10.3934/era.2024024
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility Via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317175(1-16)Online publication date: 2024
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Acharya MMohbey KRajput D(2024)Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest RecommendationIEEE Access10.1109/ACCESS.2024.335493412(11584-11596)Online publication date: 2024
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Index Terms

DAN-SNR: A Deep Attentive Network for Social-aware Next Point-of-interest Recommendation
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 1

Visions Paper, Regular Papers, SI: Blockchain in E-Commerce, and SI: Human-Centered Security, Privacy, and Trust in the Internet of Things

February 2021

534 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3441681

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2020 ACM.

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Publication History

Published: 22 December 2020

Accepted: 01 October 2020

Revised: 01 September 2020

Received: 01 April 2020

Published in TOIT Volume 21, Issue 1

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Cited By

Chen JYe WKang S(2024)Learning user preferences from Multi-Contextual Sequence influences for next POI recommendationElectronic Research Archive10.3934/era.202402432:1(486-504)Online publication date: 2024
https://doi.org/10.3934/era.2024024
Gao QHong JXu XKuang PZhou FTrajcevski G(2024)Predicting Human Mobility Via Self-Supervised Disentanglement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317175(1-16)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3317175
Acharya MMohbey KRajput D(2024)Long-Term Preference Mining With Temporal and Spatial Fusion for Point-of-Interest RecommendationIEEE Access10.1109/ACCESS.2024.335493412(11584-11596)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3354934
Yang YZhou SWeng HWang DZhang XYu DDeng S(2024)Siamese learning based on graph differential equation for Next-POI recommendationApplied Soft Computing10.1016/j.asoc.2023.111086150(111086)Online publication date: Jan-2024
https://doi.org/10.1016/j.asoc.2023.111086
Ma YWang ZHuang LWang J(2023)CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based RecommendationACM Transactions on the Web10.1145/362656918:1(1-37)Online publication date: 14-Nov-2023
https://dl.acm.org/doi/10.1145/3626569
Sun KQian TLi CMa XLi QZhong MZhu YLiu M(2023)Pre-Training Across Different Cities for Next POI RecommendationACM Transactions on the Web10.1145/360555417:4(1-27)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3605554
Wang YLee A(2023)Movie Account Recommendation on InstagramACM Transactions on Internet Technology10.1145/357985223:1(1-21)Online publication date: 13-Jan-2023
https://dl.acm.org/doi/10.1145/3579852
Huang LMa YLiu YDanny Du BWang SLi D(2023)Position-Enhanced and Time-aware Graph Convolutional Network for Sequential RecommendationsACM Transactions on Information Systems10.1145/351170041:1(1-32)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3511700
Li HYue PLi SZhang CYang C(2023)Spatio-temporal intention learning for recommendation of next point-of-interestGeo-spatial Information Science10.1080/10095020.2023.217942827:2(384-397)Online publication date: 5-Apr-2023
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Cui PYin BXu B(2023)The application of social recommendation algorithm integrating attention model in movie recommendationScientific Reports10.1038/s41598-023-43511-113:1Online publication date: 7-Oct-2023
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