research-article

Meta-path Augmented Sequential Recommendation with Contextual Co-attention Network

Authors:

Shengsheng Qian,

Changsheng XuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 2

Article No.: 52, Pages 1 - 24

https://doi.org/10.1145/3382180

Published: 22 May 2020 Publication History

Abstract

It is critical to comprehensively and efficiently learn user preferences for an effective sequential recommender system. Existing sequential recommendation methods mainly focus on modeling local preference from users’ historical behaviors, which largely ignore the global context information from the heterogeneous information network. This prevents a comprehensive user preference representation. To address these issues, we propose a joint learning approach to incorporate global context with local preferences efficiently. The proposed approach introduces meta-paths from a heterogeneous information network to capture the global context information, and the position-based self-attention mechanism is adopted to model the local preference representation efficiently. Compared with the methods that only consider the local preference, our proposed method takes the advantages of incorporating global context information, which extracts structural features that captures relevant semantics to construct users’ global preference representation for the sequential recommendation. We further adopt a co-attention mechanism to model complex interactions between global context and users’ historical behaviors for better user representations. Quantitative and qualitative experimental evaluations are conducted on nine large-scale Amazon datasets and a multi-modal Zhihu dataset. The promising results demonstrate the effectiveness of the proposed model.

References

[1]

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 International Joint Conference on Artificial Intelligence (IJCAI’13), Vol. 13. 2605--2611.

[2]

Szu-Yu Chou, Yi-Hsuan Yang, Jyh-Shing Roger Jang, and Yu-Ching Lin. 2016. Addressing cold start for next-song recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 115--118.

Digital Library

[3]

Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2016. Attention-over-attention neural networks for reading comprehension. arXiv preprint arXiv:1607.04423.

[4]

Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 135--144.

Digital Library

[5]

Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, and Bai Wang. 2015. Coupledlp: Link prediction in coupled networks. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 199--208.

Digital Library

[6]

Yong Du, Wei Wang, and Liang Wang. 2015. Hierarchical recurrent neural network for skeleton-based action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1110--1118.

[7]

Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’15). 2069--2075.

[8]

Lianli Gao, Zhao Guo, Hanwang Zhang, Xing Xu, and Heng Tao Shen. 2017. Video captioning with attention-based LSTM and semantic consistency. IEEE Trans. Multimedia 19, 9 (2017), 2045--2055.

[9]

Lianli Gao, Xiangpeng Li, Jingkuan Song, and Heng Tao Shen. 2019. Hierarchical LSTMs with adaptive attention for visual captioning. IEEE Trans. Pattern Anal. Mach. Intell. (2019).

[10]

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122.

Digital Library

[11]

Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, and Jun Zhao. 2017. An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vol. 1. 221--231.

[12]

Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In Proceedings of the IEEE 16th International Conference on Data Mining (ICDM’16). IEEE, 191--200.

[13]

Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S. Yu. 2018. Leveraging meta-path-based context for Top-N recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1531--1540.

[14]

Liang Hu, Longbing Cao, Jian Cao, Zhiping Gu, Guandong Xu, and Dingyu Yang. 2016. Learning informative priors from heterogeneous domains to improve recommendation in cold-start user domains. ACM Trans. Info. Syst. 35, 2 (2016), 13.

Digital Library

[15]

Minghao Hu, Yuxing Peng, and Xipeng Qiu. 2017. Reinforced mnemonic reader for machine comprehension. CoRR, abs/1705.02798.

[16]

Shangrong Huang, Jian Zhang, Lei Wang, and Xian-Sheng Hua. 2016. Social friend recommendation based on multiple network correlation. IEEE Trans. Multimedia 18, 2 (2016), 287--299.

Digital Library

[17]

Xiaowen Huang, Quan Fang, Shengsheng Qian, Jitao Sang, Yan Li, and Changsheng Xu. 2019. Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In Proceedings of the 27th ACM International Conference on Multimedia. 548--556.

Digital Library

[18]

Xiaowen Huang, Shengsheng Qian, Quan Fang, Jitao Sang, and Changsheng Xu. 2018. CSAN: Contextual self-attention network for user sequential recommendation. In Proceedings of the ACM Multimedia Conference on Multimedia Conference. ACM, 447--455.

Digital Library

[19]

Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining (ICDM’18). IEEE, 197--206.

[20]

Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.

[21]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning. 1188--1196.

Digital Library

[22]

Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130.

[23]

Duen-Ren Liu, Chin-Hui Lai, and Wang-Jung Lee. 2009. A hybrid of sequential rules and collaborative filtering for product recommendation. Info. Sci. 179, 20 (2009), 3505--3519.

[24]

Jian Liu, Chuan Shi, Binbin Hu, Shenghua Liu, and S. Yu Philip. 2017. Personalized ranking recommendation via integrating multiple feedbacks. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 131--143.

[25]

Qiang Liu, Shu Wu, and Liang Wang. 2017. Multi-behavioral sequential prediction with recurrent log-bilinear model. IEEE Trans. Knowl. Data Eng. 29, 6 (2017), 1254--1267.

Digital Library

[26]

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 AAAI Conference on Artificial Intelligence. 194--200.

[27]

Renfeng Ma, Qi Zhang, Jiawen Wang, Lizhen Cui, and Xuanjing Huang. 2018. Mention recommendation for multimodal microblog with cross-attention memory network. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’18). ACM, New York, NY, 195--204.

Digital Library

[28]

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52.

Digital Library

[29]

Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010. Recurrent neural network-based language model. In Proceedings of the 11th Annual Conference of the International Speech Communication Association.

[30]

Tomáš Mikolov, Stefan Kombrink, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2011. Extensions of recurrent neural network language model. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’11). IEEE, 5528--5531.

[31]

Tomas Mikolov, Stefan Kombrink, Anoop Deoras, Lukar Burget, and Jan Cernocky. 2011. Rnnlm-recurrent neural network language modeling toolkit. In Proceedings of the IEEE Automatic Speech Recognition and UnderstandingASRU Workshop. 196--201.

[32]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 701--710.

Digital Library

[33]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 452--461.

Digital Library

[34]

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

[35]

Jitao Sang, Ming Yan, and Changsheng Xu. 2018. Understanding dynamic cross-OSN associations for cold-start recommendation. IEEE Trans. Multimedia 20, 12 (2018), 3439--3451.

Digital Library

[36]

Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, and Chengqi Zhang. 2018. Disan: Directional self-attention network for rnn/cnn-free language understanding. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[37]

Chuan Shi, Binbin Hu, Xin Zhao, and Philip Yu. 2018. Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. (2018).

[38]

Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S. Yu Philip. 2017. A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29, 1 (2017), 17--37.

Digital Library

[39]

Chuan Shi, Zhiqiang Zhang, Ping Luo, Philip S. Yu, Yading Yue, and Bin Wu. 2015. Semantic path-based personalized recommendation on weighted heterogeneous information networks. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 453--462.

Digital Library

[40]

Jingkuan Song, Yuyu Guo, Lianli Gao, Xuelong Li, Alan Hanjalic, and Heng Tao Shen. 2018. From deterministic to generative: Multimodal stochastic RNNs for video captioning. IEEE Trans. Neural Netw. Learn. Syst. 30, 10 (2018), 3047--3058.

[41]

Yizhou Sun and Jiawei Han. 2012. Mining heterogeneous information networks: Principles and methodologies. Synth. Lect. Data Min. Knowl. Discov. 3, 2 (2012), 1--159.

[42]

Yizhou Sun and Jiawei Han. 2013. Mining heterogeneous information networks: A structural analysis approach. ACM SIGKDD Explor. Newslett. 14, 2 (2013), 20--28.

Digital Library

[43]

Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4, 11 (2011), 992--1003.

Digital Library

[44]

Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan, Philip S. Yu, and Xiao Yu. 2013. Pathselclus: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. ACM Trans. Knowl. Discov. Data. 7, 3 (2013), 11.

Digital Library

[45]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1067--1077.

Digital Library

[46]

Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. ACM, 565--573.

Digital Library

[47]

Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-pointer co-attention networks for recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining (KDD’18). ACM, New York, NY, 2309--2318.

Digital Library

[48]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. MIT Press, 6000--6010.

[49]

Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 403--412.

Digital Library

[50]

Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometr. Intell. Lab. Syst. 2, 1--3 (1987), 37--52.

[51]

Caiming Xiong, Victor Zhong, and Richard Socher. 2016. Dynamic coattention networks for question answering. Retrieved from http://arxiv.org/abs/1611.01604.

[52]

Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In Proceedings of the International Conference on Machine Learning. 2048--2057.

Digital Library

[53]

Zhenxing Xu, Ling Chen, Yimeng Dai, and Gencai Chen. 2017. A dynamic topic model and matrix factorization-based travel recommendation method exploiting ubiquitous data. IEEE Trans. Multimedia 19, 8 (2017), 1933--1945.

Digital Library

[54]

Yongjian Yang, Yuanbo Xu, En Wang, Jiayu Han, and Zhiwen Yu. 2018. Improving existing collaborative filtering recommendations via serendipity-based algorithm. IEEE Trans. Multimedia 20, 7 (2018), 1888--1900.

[55]

Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. 2014. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 283--292.

Digital Library

[56]

Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, and Zhongfei Mark Zhang. 2016. Deepintent: Learning attentions for online advertising with recurrent neural networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1295--1304.

Digital Library

[57]

Xishan Zhang, Jia Jia, Ke Gao, Yongdong Zhang, Dongming Zhang, Jintao Li, and Qi Tian. 2017. Trip outfits advisor: Location-oriented clothing recommendation. IEEE Trans. Multimedia 19, 11 (2017), 2533--2544.

[58]

Yongfeng Zhang, Qingyao Ai, Xu Chen, and W. Bruce Croft. 2017. Joint representation learning for top-n recommendation with heterogeneous information sources. In Proceedings of the ACM on Conference on Information and Knowledge Management. ACM, 1449--1458.

[59]

Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, and Tie-Yan Liu. 2014. Sequential click prediction for sponsored search with recurrent neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 14. 1369--1375.

[60]

Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, and Dik Lun Lee. 2017. Meta-graph-based recommendation fusion over heterogeneous information networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 635--644.

Digital Library

[61]

Zhou Zhao, Qifan Yang, Hanqing Lu, Tim Weninger, Deng Cai, Xiaofei He, and Yueting Zhuang. 2018. Social-aware movie recommendation via multimodal network learning. IEEE Trans. Multimedia 20, 2 (2018), 430--440.

Digital Library

[62]

Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 425--434.

[63]

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.

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Index Terms

Meta-path Augmented Sequential Recommendation with Contextual Co-attention Network
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 2

May 2020

390 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3401894

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Publication History

Published: 22 May 2020

Online AM: 07 May 2020

Accepted: 01 February 2020

Revised: 01 February 2020

Received: 01 September 2019

Published in TOMM Volume 16, Issue 2

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National Natural Science Foundation of China
Key Research Program of Frontier Sciences, CAS
National Key Research and Development Program of China
K. C. Wong Education Foundation

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Cited By

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https://doi.org/10.1016/j.engappai.2024.109471
Niu YXing XJia ZLiu RXin MCui JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Diffusion Recommendation with Implicit Sequence InfluenceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651951(1719-1725)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651951
Wang BDeng XZhang GLi H(2024)Auxiliary Information Enhanced Multi-Task Recommendation for Tourist Attractions2024 9th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS61882.2024.10602823(291-296)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICCCS61882.2024.10602823
Watanabe SIto HMatsubara MMorishima A(2024)Human-in-the-loop latent space learning for biblio-record-based literature managementInternational Journal on Digital Libraries10.1007/s00799-023-00389-825:1(123-136)Online publication date: 1-Mar-2024
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Tang HZhang FXu XZhang JLiu D(2022)A Dual-View Knowledge Enhancing Self-Attention Network for Sequential Recommendation2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI56018.2022.00128(832-839)Online publication date: Oct-2022
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