research-article

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations

Authors:

Yuanzhang Xiao, and

Linqi SongAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

Pages 1389 - 1398

https://doi.org/10.1145/3511808.3557354

Published: 17 October 2022 Publication History

Abstract

The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI) recommendations and computing resource-aware mobile app recommendations. In many such recommendation scenarios, contexts are drifting over time. For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time. However, most existing graph-based collaborative filtering methods are designed under the assumption of static features. Therefore, they would require frequent retraining and/or yield graphical models burgeoning in sizes, impeding their suitability for context-drifting recommendations.

In this work, we propose a specifically tailor-made Hybrid Static and Adaptive Graph Embedding (HySAGE) network for context-drifting recommendations. Our key idea is to disentangle the relatively static user-item interaction and rapidly drifting contextual features. Specifically, our proposed HySAGE network learns a relatively static graph embedding from user-item interaction and an adaptive embedding from drifting contextual features. These embeddings are incorporated into an interest network to generate the user interest in some certain context. We adopt an interactive attention module to learn the interactions among static graph embeddings, adaptive contextual embeddings, and user interest, helping to achieve a better final representation. Extensive experiments on real-world datasets demonstrate that HySAGE significantly improves the performance of the existing state-of-the-art recommendation algorithms.

References

[1]

Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-aware recommender systems. In Recommender systems handbook. Springer, 217--253.

[2]

Mohammad Aliannejadi and Fabio Crestani. 2018. Personalized context-aware point of interest recommendation. ACM Transactions on Information Systems (TOIS), Vol. 36, 4 (2018), 1--28.

Digital Library

[3]

Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft. 2021. Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants. ACM Transactions on Information Systems (TOIS), Vol. 39, 3 (2021), 1--30.

Digital Library

[4]

Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering, Vol. 30, 9 (2018), 1616--1637.

Digital Library

[5]

Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020b. Efficient non-sampling factorization machines for optimal context-aware recommendation. In Proceedings of The Web Conference 2020. 2400--2410.

Digital Library

[6]

Wanyu Chen, Fei Cai, Honghui Chen, and Maarten de Rijke. 2019. Joint Neural Collaborative Filtering for Recommender Systems. arXiv preprint arXiv:1907.03459 (2019).

[7]

Weijing Chen, Weigang Chen, and Linqi Song. 2020a. Enhancing Deep Multimedia Recommendations Using Graph Embeddings. In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 161--166.

[8]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. arXiv preprint arXiv:1902.07243 (2019).

[9]

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482 (2019).

[10]

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

Digital Library

[11]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).

[12]

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. 770--778.

[13]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639--648.

Digital Library

[14]

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.

Digital Library

[15]

Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[16]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[17]

Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web. 661--670.

Digital Library

[18]

Yang Li, Yadan Luo, Zheng Zhang, Shazia Sadiq, and Peng Cui. 2019. Context-aware attention-based data augmentation for poi recommendation. In 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW). IEEE, 177--184.

[19]

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1532--1543.

[20]

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. 701--710.

Digital Library

[21]

Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019).

[22]

Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International conference on data mining. IEEE, 995--1000.

Digital Library

[23]

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. 452--461.

Digital Library

[24]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. 285--295.

Digital Library

[25]

Zhulin Tao, Yinwei Wei, Xiang Wang, Xiangnan He, Xianglin Huang, and Tat-Seng Chua. 2020. MGAT: multimodal graph attention network for recommendation. Information Processing & Management, Vol. 57, 5 (2020), 102277.

[26]

Flavian Vasile, Elena Smirnova, and Alexis Conneau. 2016. Meta-prod2vec: Product embeddings using side-information for recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 225--232.

Digital Library

[27]

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. 5998--6008.

[28]

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17. ACM, 12.

Digital Library

[29]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 165--174.

Digital Library

[30]

Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In Proceedings of the 27th ACM International Conference on Multimedia. 1437--1445.

Digital Library

[31]

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.

Digital Library

[32]

Xin Xin, Bo Chen, Xiangnan He, Dong Wang, Yue Ding, and Joemon Jose. 2019. CFM: Convolutional Factorization Machines for Context-Aware Recommendation. In IJCAI, Vol. 19. 3926--3932.

[33]

Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019. Graph Contextualized Self-Attention Network for Session-based Recommendation. In IJCAI, Vol. 19. 3940--3946.

[34]

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 974--983.

Digital Library

[35]

Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2020. TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1921--1924.

Digital Library

[36]

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 2017 ACM on Conference on Information and Knowledge Management. ACM, 1449--1458.

Digital Library

[37]

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5941--5948.

Digital Library

[38]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1059--1068.

Digital Library

Cited By

Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Luo SWang JZhou AMa LSong L(2024)Large Language Models Augmented Rating Prediction in Recommender SystemICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447514(7960-7964)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447514
Luo SXiao YLiu YLi CSong L(2022)Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation2022 5th International Conference on Information Communication and Signal Processing (ICICSP)10.1109/ICICSP55539.2022.10050588(1-6)Online publication date: 26-Nov-2022
https://doi.org/10.1109/ICICSP55539.2022.10050588
Show More Cited By

Index Terms

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Graph Embedding Based Recommendation Techniques on the Knowledge Graph
UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

This paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two ...
Read More
Hierarchical Multi-view Attention for Neural Review-Based Recommendation
Natural Language Processing and Chinese Computing
Abstract
Many E-commerce platforms allow users to write their opinions towards products, and these reviews contain rich semantic information for users and items. Hence review analysis has been widely used in recommendation systems. However, most existing ...
Read More
CSLIM: contextual SLIM recommendation algorithms
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

Context-aware recommender systems (CARS) take contextual conditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Hong Kong RGC
Technological Breakthrough Project of Science, Technology and Innovation Commission of Shenzhen Municipality
InnoHK initiative, the Government of the HKSAR, Laboratory for AI-Powered Financial Technologies
Hong Kong UGC Special Virtual Teaching and Learning (VTL)
Changsha Science and Technology Program International and Regional Science and Technology Cooperation Project
Tencent AI Lab Rhino-Bird Gift Fund

Conference

CIKM '22

Sponsor:

CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

Acceptance Rates

CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
151
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)1

Other Metrics

View Author Metrics

Citations

Cited By

Li YLiu KSatapathy RWang SCambria E(2024)Recent Developments in Recommender Systems: A Survey [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2024.336398419:2(78-95)Online publication date: May-2024
https://doi.org/10.1109/MCI.2024.3363984
Luo SWang JZhou AMa LSong L(2024)Large Language Models Augmented Rating Prediction in Recommender SystemICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447514(7960-7964)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447514
Luo SXiao YLiu YLi CSong L(2022)Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation2022 5th International Conference on Information Communication and Signal Processing (ICICSP)10.1109/ICICSP55539.2022.10050588(1-6)Online publication date: 26-Nov-2022
https://doi.org/10.1109/ICICSP55539.2022.10050588
Luo SXiao YZhang XLiu YDing WSong L(undefined)PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-TrainingACM Transactions on Intelligent Systems and Technology10.1145/3664927
https://dl.acm.org/doi/10.1145/3664927

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents