Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (2): 281-294.doi: 10.1007/s11390-020-9956-9

• Special Section on Learning and Mining in Dynamic Environments • Previous Articles     Next Articles

Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation

Yang Liu, Zhi Li, Wei Huang, Tong Xu*, En-Hong Chen, Fellow, CCF, Senior Member, IEEE        

  1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China Hefei 230027, China
  • Received:2019-08-20 Revised:2019-12-22 Online:2020-03-05 Published:2020-03-18
  • Contact: Tong Xu E-mail:tongxu@ustc.edu.cn
  • About author:Yang Liu received her B.E. degree in information security from University of Science and Technology of China (USTC), Hefei, in 2016. She is currently working toward her Master's degree in the Anhui Province Key Laboratory of Big Data Analysis and Application, USTC, Hefei. Her research interests include deep learning and its application in recommender systems.
  • Supported by:
    This work was partially supported by the National Key Research and Development Program of China under Grant No. 2018YFB1402600, the National Natural Science Foundation of China under Grant Nos. 61703386 and U1605251, and the MSRA (Microsoft Research Asia) Collaborative Research Project.

Recent years have witnessed the rapid development of online social platforms, which effectively support the business intelligence and provide services for massive users. Along this line, large efforts have been made on the socialaware recommendation task, i.e., leveraging social contextual information to improve recommendation performance. Most existing methods have treated social relations in a static way, but the dynamic influence of social contextual information on users' consumption choices has been largely unexploited. To that end, in this paper, we conduct a comprehensive study to reveal the dynamic social influence on users' preferences, and then we propose a deep model called Dynamic Social-Aware Recommender System (DSRS) to integrate the users' structural and temporal social contexts to address the dynamic socialaware recommendation task. DSRS consists of two main components, i.e., the social influence learning (SIL) and dynamic preference learning (DPL). Specifically, in the SIL module, we arrange social graphs in a sequential order and borrow the power of graph convolution networks (GCNs) to learn social context. Moreover, we design a structural-temporal attention mechanism to discriminatively model the structural social influence and the temporal social influence. Then, in the DPL part, users' individual preferences are learned dynamically by recurrent neural networks (RNNs). Finally, with a prediction layer, we combine the users' social context and dynamic preferences to generate recommendations. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority and effectiveness of our proposed model compared with the state-of-the-art methods.

Key words: recommender system; social influence; sequential recommendation;

[1] Mnih A, Salakhutdinov R R. Probabilistic matrix factorization. In Proc. the 21st Annual Conference on Neural Information Processing Systems, December 2007, pp.1257-1264.
[2] Koren Y. Factorization meets the neighborhood:A multifaceted collaborative filtering model. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2008, pp.426-434.
[3] Rendle S, Freudenthaler C, Gantner Z et al. BPR:Bayesian personalized ranking from implicit feedback. In Proc. the 25th Conference on Uncertainty in Artificial Intelligence, June 2009, pp.452-461.
[4] Ma H, Yang H, Lyu M R et al. SoRec:Social recommendation using probabilistic matrix factorization. In Proc. the 17th ACM Conference on Information and Knowledge Management, October 2008, pp.931-940.
[5] Ma H, Zhou D, Liu C et al. Recommender systems with social regularization. In Proc. the 4th ACM International Conference on Web Search and Data Mining, February 2011, pp.287-296.
[6] Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In Proc. the 4th ACM Conference on Recommender Systems, September 2010, pp.135-142.
[7] Guo G, Zhang J, Yorke-Smith N. TrustSVD:Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proc. the 29th AAAI Conference on Artificial Intelligence, January 2015, pp.123-129.
[8] Jiang M, Cui P, Wang F et al. Scalable recommendation with social contextual information. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11):2789-2802.
[9] Biao C, Tong X, Qi L et al. Study on information diffusion analysis in social networks and its applications. International Journal of Automation and Computing, 2018, 15(4):377-401.
[10] Huang Z, Pan Z, Liu Q et al. An Ad CTR prediction method based on feature learning of deep and shallow layers. In Proc. the 2017 ACM on Conference on Information and Knowledge Management, November 2017, pp.2119-2122.
[11] Guo H, Tang R, Ye Y et al. DeepFM:A factorizationmachine based neural network for CTR prediction. arXiv:1703.04247, 2017. https://arxiv.org/abs/1703.04247,Dec.2019.
[12] He X, Liao L, Zhang H et al. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.173-182.
[13] Yu F, Liu Q, Wu S et al. A dynamic recurrent model for next basket recommendation. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.729-732.
[14] Hidasi B, Karatzoglou A, Baltrunas L et al. Sessionbased recommendations with recurrent neural networks. arXiv:1511.06939, 2015. https://arxiv.org/abs/1511.06939,Dec.2019.
[15] Li J, Ren P, Chen Z et al. Neural attentive session-based recommendation. In Proc. the 2017 ACM Conference on Information and Knowledge Management, November 2017, pp.1419-1428.
[16] Hidasi B, Karatzoglou A. Recurrent neural networks with top-k gains for session-based recommendations. In Proc. the 27th ACM International Conference on Information and Knowledge Management, October 2018, pp.843-852.
[17] Lei C, Liu D, Li W et al. Comparative deep learning of hybrid representations for image recommendations. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.2545-2553.
[18] Hou M, Wu L, Chen E et al. Explainable fashion recommendation:A semantic attribute region guided approach. arXiv:1905.12862, 2019. https://arxiv.org/pdf/1905.12862.pdf,Dec.2019.
[19] Kim D, Park C, Oh J et al. Convolutional matrix factorization for document context-aware recommendation. In Proc. the 10th ACM Conference on Recommender Systems, September 2016, pp.233-240.
[20] Wang Q, Li S, Chen G. Word-driven and context-aware review modeling for recommendation. In Proc. the 27th ACM International Conference on Information and Knowledge Management, October 2018, pp.1859-1862.
[21] Aral S, Muchnik L, Sundararajan A. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 2009, 106(51):21544-21549.
[22] Sun P, Wu L, Wang M. Attentive recurrent social recommendation. In Proc. the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2018, pp.185-194.
[23] Wu L, Sun P, Hong R et al. Collaborative neural social recommendation. IEEE Transactions on Systems, Man, and Cybernetics:Systems. doi:10.1109/TSMC.2018.2872842.
[24] Wu L, Sun P, Hong R et al. SocialGCN:An efficient graph convolutional network based model for social recommendation. arXiv:1811.02815, 2018. https://arxiv.org/abs/1811.02815,Dec.2019.
[25] Fan W, Ma Y, Li Q et al. Graph neural networks for social recommendation. arXiv:1902.07243, 2019. https://arxiv.org/pdf/1902.07243.pdf,Dec.2019.
[26] Qiu J, Tang J, Ma H et al. DeepInf:Social influence prediction with deep learning. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2018, pp.2110-2119.
[27] Wang H, Xu T, Liu Q et al. MCNE:An end-to-end framework for learning multiple conditional network representations of social network. In Proc. the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 2019, pp.1064-1072.
[28] Xu T, Zhu H S, Zhong H et al. Exploiting the dynamic mutual influence for predicting social event participation. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(6):1122-1135.
[29] Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized Markov chains for next-basket recommendation. In Proc. the 19th International Conference on World Wide Web, April 2010, pp.811-820.
[30] Wang P, Guo J, Lan Y et al. Learning hierarchical representation model for next basket recommendation. In Proc. the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2015, pp.403-412.
[31] Wu C Y, Ahmed A, Beutel A et al. Recurrent recommender networks. In Proc. the 10th ACM International Conference on Web Search and Data Mining, February 2017, pp.495-503.
[32] Li Z, Zhao H, Liu Q et al. Learning from history and present:Next-item recommendation via discriminatively exploiting user behaviors. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2018, pp.1734-1743.
[33] Sordoni A, Bengio Y, Vahabi H et al. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In Proc. the 24th ACM International on Conference on Information and Knowledge Management, October 2015, pp.553-562.
[34] Manotumruksa J, Macdonald C, Ounis I. A contextual attention recurrent architecture for context-aware venue recommendation. In Proc. the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2018, pp.555-564.
[35] Smirnova E, Vasile F. Contextual sequence modeling for recommendation with recurrent neural networks. In Proc. the 2nd Workshop on Deep Learning for Recommender Systems, August 2017, pp.2-9.
[36] Manotumruksa J, Macdonald C, Ounis I. A contextual attention recurrent architecture for context-aware venue recommendation. In Proc. the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2018, pp.555-564.
[37] Bruna J, Zaremba W, Szlam A et al. Spectral networks and locally connected networks on graphs. arXiv:1312.6203, 2013. https://arxiv.org/abs/1312.6203,Dec.2013.
[38] Feng C, Liu Z, Lin S et al. Attention-based graph convolutional network for recommendation system. In Proc. the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, pp.7560-7564.
[39] Zhang J, Shi X, Xie J et al. GaAN:Gated attention networks for learning on large and spatiotemporal graphs. arXiv:1803.07294, 2018. https://arxiv.org/pdf/1803.07294.pdf,Dec.2019.
[40] Veličković P, Cucurull G, Casanova A et al. Graph attention networks. arXiv:1710.10903, 2017. https://arxiv.org/abs/1710.10903,Dec.2019.
[41] Wu L K, Li Z, Zhao H K et al. Estimating early fundraising performance of innovations via graph-based market environment model. arXiv:1912.06767, 2019. https://arxiv.org/abs/1912.06767v1,Dec.2019.
[42] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8):1735-1780.
[43] Kingma D P, Ba J. Adam:A method for stochastic optimization. arXiv:1412.6980, 2014. https://arxiv.org/abs/1412.6980,Dec.2019.
[44] Richardson M, Agrawal R, Domingos P. Trust management for the semantic Web. In Proc. the 2nd International Semantic Web Conference, October 2003, pp.351-368.
[45] Scellato S, Noulas A, Mascolo C. Exploiting place features in link prediction on location-based social networks. In Proc. the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2011, pp.1046-1054.
[1] Reza Jafari Ziarani, Reza Ravanmehr. Serendipity in Recommender Systems: A Systematic Literature Review [J]. Journal of Computer Science and Technology, 2021, 36(2): 375-396.
[2] Yu-Yao Liu, Bo Yang, Hong-Bin Pei, Jing Huang. Neural Explainable Recommender Model Based on Attributes and Reviews [J]. Journal of Computer Science and Technology, 2020, 35(6): 1446-1460.
[3] Fu-Zhen Zhuang, Ying-Min Zhou, Hao-Chao Ying, Fu-Zheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong. Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining [J]. Journal of Computer Science and Technology, 2020, 35(2): 305-319.
[4] Pengfei Wang, Yongfeng Zhang, Shuzi Niu, Jiafeng Guo. Modeling Temporal Dynamics of Users' Purchase Behaviors for Next Basket Prediction [J]. Journal of Computer Science and Technology, 2019, 34(6): 1230-1240.
[5] Qi Liu, Hong-Ke Zhao, Le Wu, Zhi Li, En-Hong Chen. Illuminating Recommendation by Understanding the Explicit Item Relations [J]. , 2018, 33(4): 739-755.
[6] Mehdi Azaouzi, Lotfi Ben Romdhane. An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions [J]. , 2018, 33(2): 286-304.
[7] Ming-Xin Gan, Lily Sun, Rui Jiang. Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations [J]. , 2016, 31(3): 577-594.
[8] Xin Xin, Chin-Yew Lin, Xiao-Chi Wei, He-Yan Huang. When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework [J]. , 2015, 30(4): 917-932.
[9] Xiang-Liang Zhang, Tak Man Desmond Lee, and Georgios Pitsilis. Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering [J]. , 2013, 28(4): 616-624.
[10] Hui-Feng Sun, Jun-Liang Chen, Gang Yu, Chuan-Chang Liu, Yong Peng, Guang Chen, and Bo Cheng. JacUOD: A New Similarity Measurement for Collaborative Filtering [J]. , 2012, 27(6): 1252-1260.
[11] Marcelo G. Armentano, Daniela Godoy, and Analia Amandi. Topology-Based Recommendation of Users in Micro-Blogging Communities [J]. , 2012, 27(3): 624-634.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[3] Li Wanxue;. Almost Optimal Dynamic 2-3 Trees[J]. , 1986, 1(2): 60 -71 .
[4] C.Y.Chung; H.R.Hwa;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[5] Zhang Cui; Zhao Qinping; Xu Jiafu;. Kernel Language KLND[J]. , 1986, 1(3): 65 -79 .
[6] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[7] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[8] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[9] Zheng Guoliang; Li Hui;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .
[10] Huang Xuedong; Cai Lianhong; Fang Ditang; Chi Bianjin; Zhou Li; Jiang Li;. A Computer System for Chinese Character Speech Input[J]. , 1986, 1(4): 75 -83 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved