research-article

Unsupervised Proxy Selection for Session-based Recommender Systems

Authors:

Hwanjo YuAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 327 - 336

https://doi.org/10.1145/3404835.3462958

Published: 11 July 2021 Publication History

Abstract

Session-based Recommender Systems (SRSs) have been actively developed to recommend the next item of an anonymous short item sequence (i.e., session). Unlike sequence-aware recommender systems where the whole interaction sequence of each user can be used to model both the short-term interest and the general interest of the user, the absence of user-dependent information in SRSs makes it difficult to directly derive the user's general interest from data. Therefore, existing SRSs have focused on how to effectively model the information about short-term interest within the sessions, but they are insufficient to capture the general interest of users. To this end, we propose a novel framework to overcome the limitation of SRSs, named ProxySR, which imitates the missing information in SRSs (i.e., general interest of users) by modeling proxies of sessions. ProxySR selects a proxy for the input session in an unsupervised manner, and combines it with the encoded short-term interest of the session. As a proxy is jointly learned with the short-term interest and selected by multiple sessions, a proxy learns to play the role of the general interest of a user and ProxySR learns how to select a suitable proxy for an input session. Moreover, we propose another real-world situation of SRSs where a few users are logged-in and leave their identifiers in sessions, and a revision of ProxySR for the situation. Our experiments on real-world datasets show that ProxySR considerably outperforms the state-of-the-art competitors, and the proxies successfully imitate the general interest of the users without any user-dependent information.

Supplementary Material

MP4 File (Unsupervised Proxy Selection for Session-based Recommender Systems.mp4)

Presentation video

Download
16.61 MB

References

[1]

O. Celma. 2010. Music Recommendation and Discovery in the Long Tail .Springer.

[2]

Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP. 1724--1734.

[3]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171--4186.

[4]

Evgeny Frolov and Ivan Oseledets. 2019. HybridSVD: When Collaborative Information is Not Enough. In Proceedings of the 13th ACM Conference on Recommender Systems. 331--339.

Digital Library

[5]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778.

[6]

Yun He, Jianling Wang, Wei Niu, and James Caverlee. 2019. A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1481--1490.

Digital Library

[7]

Balázs Hidasi, Alexandros Karatzoglou, L. Baltrunas, and D. Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In Proceedings of the 4th International Conference on Learning Representations.

[8]

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.

Digital Library

[9]

Linmei Hu, Chen Li, Chuan Shi, Cheng Yang, and Chao Shao. 2020. Graph Neural News Recommendation with Long-term and Short-term Interest Modeling. Information Processing & Management, Vol. 57, 2 (2020), 102142.

Digital Library

[10]

Corey Brian Jackson, Kevin Crowston, and Carsten Østerlund. 2018. Did They Login? Patterns of Anonymous Contributions in Online Communities. Proceedings of the ACM on Human-Computer Interaction, Vol. 2, Article 77 (Nov. 2018), 16 pages.

Digital Library

[11]

Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In 5th International Conference on Learning Representations, ICLR.

[12]

W. Kang and J. McAuley. 2018. Self-Attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). 197--206.

[13]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015.

[14]

Yehuda Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 426--434.

Digital Library

[15]

Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural Attentive Session-Based Recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1419--1428.

Digital Library

[16]

Defu Lian, Qi Liu, and Enhong Chen. 2020. Personalized Ranking with Importance Sampling. In Proceedings of The Web Conference 2020. 1093--1103.

Digital Library

[17]

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. 2181--2187.

[18]

Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1831--1839.

Digital Library

[19]

Anjing Luo, Pengpeng Zhao, Yanchi Liu, Fuzhen Zhuang, Deqing Wang, Jiajie Xu, Junhua Fang, and Victor S. Sheng. 2020. Collaborative Self-Attention Network for Session-based Recommendation. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. 2591--2597.

[20]

Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, and Mark Coates. 2020. Memory Augmented Graph Neural Networks for Sequential Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 5045--5052.

[21]

Andrew L. Maas, Awni Y. Hanuun, and Andrew Y. Ng. 2013. Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the 30th International Conference on Machine Learning.

[22]

Y. Movshovitz-Attias, A. Toshev, T. K. Leung, S. Ioffe, and S. Singh. 2017. No Fuss Distance Metric Learning Using Proxies. In 2017 IEEE International Conference on Computer Vision (ICCV). 360--368.

[23]

Katherine Panciera, Reid Priedhorsky, Thomas Erickson, and Loren Terveen. 2010. Lurking? Cyclopaths? A Quantitative Lifecycle Analysis of User Behavior in a Geowiki. Conference on Human Factors in Computing Systems - Proceedings, Vol. 3, 1917--1926.

Digital Library

[24]

Chanyoung Park, Donghyun Kim, Xing Xie, and Hwanjo Yu. 2018. Collaborative Translational Metric Learning. In 2018 IEEE International Conference on Data Mining (ICDM). 367--376.

[25]

Q. Qian, L. Shang, B. Sun, J. Hu, T. Tacoma, H. Li, and R. Jin. 2019. SoftTriple Loss: Deep Metric Learning Without Triplet Sampling. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 6449--6457.

[26]

Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi YIn. 2019. Rethinking the Item Order in Session-Based Recommendation with Graph Neural Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 579--588.

Digital Library

[27]

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing Session-Based Recommendations with Hierarchical Recurrent Neural Networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 130--137.

Digital Library

[28]

Muhammad Ehsan Rana, M. Ahsan, and Wan Nurhayati Wanabrahman. 2019. A Detailed Analysis on the Use of Gamification in Cloud Based Learning Management Systems. Journal of Theoretical and Applied Information Technology, Vol. 97 (04 2019), 2179--2194.

[29]

Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten Rijke. 2019. RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence. 4806--4813.

Digital Library

[30]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian Personalized Ranking from Implicit Feedback. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009 (2012).

[31]

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. 811--820.

Digital Library

[32]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, Vol. 15, 56 (2014), 1929--1958.

Digital Library

[33]

Qiaoqiao Tan and Fang'ai Liu. 2019. Recommendation Based on Users' Long-Term and Short-Term Interests with Attention. Mathematical Problems in Engineering, Vol. 2019 (2019), 1--13.

[34]

Jiaxi Tang and Ke Wang. 2018. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 565--573.

Digital Library

[35]

Laurens van der Maaten and Geoffrey Hinton. 2008. Viualizing Data Using t-SNE. Journal of Machine Learning Research, Vol. 9 (11 2008), 2579--2605.

[36]

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 Advances in Neural Information Processing Systems 30 (NIPS 2017). 5998--6008.

[37]

Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019. A Collaborative Session-Based Recommendation Approach with Parallel Memory Modules. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 345--354.

Digital Library

[38]

Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu. 2020. Global Context Enhanced Graph Neural Networks for Session-Based Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 169--178.

Digital Library

[39]

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. 1112--1119.

[40]

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

[41]

Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, and Xing Xie. 2019. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. 4213--4219.

[42]

F. Yuan, X. He, Haochuan Jiang, G. Guo, Jian Xiong, Zhezhao Xu, and Yilin Xiong. 2020. Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation. In Proceedings of The Web Conference 2020.

Digital Library

[43]

Shuai Zhang, Yi Tay, Lina Yao, and Aixin Sun. 2018. Next Item Recommendation with Self-attention. arXiv preprint arXiv:1808.06414 (2018).

[44]

Wei Zhao, Benyou Wang, Jianbo Ye, Yongqiang Gao, Min Yang, and Xiaojun Chen. 2018. PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. 3676--3682.

Cited By

Ding YZhang ZWang B(2025)Category-integrated Dual-Task Graph Neural Networks for session-based recommendationExpert Systems with Applications10.1016/j.eswa.2024.125784263(125784)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125784
Vuong Nguyen L(2024)Classifications, evaluation metrics, datasets, and domains in recommendation servicesInternational Journal of Hybrid Intelligent Systems10.3233/HIS-24000320:2(85-100)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.3233/HIS-240003
Li XTang YGuo CDing P(2024)Attribute-Enhanced Hypergraph Neural Networks for Session-based Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651027(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651027
Show More Cited By

Index Terms

Unsupervised Proxy Selection for Session-based Recommender Systems
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Learning from implicit feedback
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering
      2. Personalization

Recommendations

Diversification in session-based news recommender systems
Abstract
Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites, most of users are anonymous and the only available data is sequences of items in anonymous ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 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

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '21

Sponsor:

SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
477
Total Downloads

Downloads (Last 12 months)30
Downloads (Last 6 weeks)2

Reflects downloads up to 10 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ding YZhang ZWang B(2025)Category-integrated Dual-Task Graph Neural Networks for session-based recommendationExpert Systems with Applications10.1016/j.eswa.2024.125784263(125784)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125784
Vuong Nguyen L(2024)Classifications, evaluation metrics, datasets, and domains in recommendation servicesInternational Journal of Hybrid Intelligent Systems10.3233/HIS-24000320:2(85-100)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.3233/HIS-240003
Li XTang YGuo CDing P(2024)Attribute-Enhanced Hypergraph Neural Networks for Session-based Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651027(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651027
Liu WZhang ZDing YWang B(2024)Global heterogeneous graph enhanced category-aware attention network for session-based recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122907243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122907
Nguyen LVo QPhan T(2024)A Survey of Recommendation Systems: Datasets, Evaluation Methods, and Application DomainsIntelligent Systems Design and Applications10.1007/978-3-031-64779-6_30(311-322)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-64779-6_30
Veeramani HThapa SNaseem U(2023)Temporally Dynamic Session-Keyword Aware Sequential Recommendation System2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00027(157-164)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00027
Zhang ZWang B(2023)Graph Spring Network and Informative Anchor Selection for session-based recommendationNeural Networks10.1016/j.neunet.2022.12.003159:C(43-56)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.neunet.2022.12.003
Xiao XZhou WWen J(2023)LSIANInformation Sciences: an International Journal10.1016/j.ins.2023.119138642:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119138
Yuan PSun YWang H(2022)Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture SearchMathematics10.3390/math1016289510:16(2895)Online publication date: 12-Aug-2022
https://doi.org/10.3390/math10162895
Hyun DPark CCho JYu HAl Hasan MXiong L(2022)Beyond Learning from Next ItemProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557415(812-821)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557415
Show More Cited By

View Options

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.

Figures

Tables

Media

View Table of Conten