research-article

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

Authors:

Xiuqiang HeAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 1243 - 1252

https://doi.org/10.1145/3459637.3482297

Published: 30 October 2021 Publication History

Abstract

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored. In this work, we show that the choice of loss function as well as negative sampling ratio is equivalently important. More specifically, we propose the cosine contrastive loss (CCL) and further incorporate it to a simple unified CF model, dubbed SimpleX. Extensive experiments have been conducted on 10 benchmark datasets and compared with 28 existing CF models in total. Surprisingly, the results show that, under our CCL loss and a large negative sampling ratio, SimpleX can surpass most sophisticated state-of-the-art models by a large margin (e.g., max 48.5% improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only serve as a simple strong baseline to foster future research on CF, but also shed light on the potential research direction towards improving loss function and negative sampling.

Supplementary Material

MP4 File (meeting_03.mp4)

Presentation video of CIKM 2021 paper "SimpleX: A Simple and Strong Baseline for Collaborative Filtering"

Download
13.94 MB

References

[1]

Rianne van den Berg, Thomas N Kipf, and Max Welling. 2018. Graph Convolutional Matrix Completion. In KDD'18 Deep Learning Day.

[2]

Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient Neural Matrix Factorization without Sampling for Recommendation. ACM Transactions on Information Systems (TOIS) 38, 2 (2020), 1--28.

Digital Library

[3]

Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 335--344.

Digital Library

[4]

Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020. Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). 27--34.

[5]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM conference on Recommender Systems (RecSys). 191--198.

Digital Library

[6]

Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. 2019. Rein-forced Negative Sampling for Recommendation with Exposure Data. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI). 2230--2236.

[7]

Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative Memory Network for Recommendation Systems. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR). 515--524.

[8]

Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality Reduction by Learning an Invariant Mapping. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). 1735--1742.

Digital Library

[9]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems (NeurIPS). 1024--1034.

[10]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong 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 (SIGIR). 639--648.

Digital Library

[11]

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 (WWW). 173--182.

Digital Library

[12]

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 (WWW). 193--201.

Digital Library

[13]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM). 263--272.

Digital Library

[14]

Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, and Yue Gao. 2020. Dual Channel Hypergraph Collaborative Filtering. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 2020--2029.

Digital Library

[15]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Tech-niques for Recommender Systems. Computer 42, 8 (2009), 30--37.

Digital Library

[16]

Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW). 689--698.

Digital Library

[17]

Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. In International Conference on Machine Learning (ICML). 4212--4221.

[18]

Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning Disentangled Representations for Recommendation. In Advances in Neural Information Processing Systems (NeurIPS). 5711--5722.

[19]

Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. 2017. A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM). 1429--1438.

Digital Library

[20]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems (NeurIPS). 3111--3119.

[21]

Xia Ning and George Karypis. 2011. SLIM: Sparse Linear Methods for Top-N Recommender Systems. In IEEE 11th International Conference on Data Mining (ICDM). 497--506.

[22]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452--461.

Digital Library

[23]

Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I. Nikolenko. 2020. RecVAE: A New Variational Autoencoder for Top-N Recommen-dations with Implicit Feedback. In The Thirteenth ACM International Conference on Web Search and Data Mining (WSDM). 528--536.

[24]

Brent Smith and Greg Linden. 2017. Two Decades of Recommender Systems at Amazon.com. IEEE Internet Comput. 21, 3 (2017), 12--18.

Digital Library

[25]

Jinbo Song, Chao Chang, Fei Sun, Xinbo Song, and Peng Jiang. 2020. NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation. arXiv preprint arXiv:2010.12256 (2020).

[26]

Harald Steck. 2019. Embarrassingly Shallow Autoencoders for Sparse Data. In The World Wide Web Conference (WWW). 3251--3257.

[27]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell. 2009 (2009), 421425:1--421425:19.

[28]

Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Rep-resentations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). 1441--1450.

Digital Library

[29]

Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, et al. 2020. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 2030--2039.

Digital Library

[30]

Jianing Sun, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Xiuqiang He, Chen Ma, and Mark Coates. 2020. Neighbor Interaction Aware Graph Convolution Networks for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1289--1298.

Digital Library

[31]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In International Con-ference on Learning Representations (ICLR).

[32]

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 (SIGIR). 165--174.

Digital Library

[33]

Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 1001--1010.

Digital Library

[34]

Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019. NPA: Neural News Recommendation with Personalized Attention. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 2576--2584.

Digital Library

[35]

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 726--735.

Digital Library

[36]

Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaoming Wang, Taibai Xu, and Ed H Chi. 2020. Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. In Companion Proceedings of the Web Conference (WWW). 441--447.

[37]

Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. HOP-Rec: High-order Proximity for Implicit Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys). 140--144.

Digital Library

[38]

Shuo Yang, Wei Yu, Ying Zheng, Hongxun Yao, and Tao Mei. 2019. Adaptive Semantic-Visual Tree for Hierarchical Embeddings. In Proceedings of the 27th ACM International Conference on Multimedia (MM). 2097--2105.

Digital Library

[39]

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 (KDD). 974--983.

Digital Library

[40]

Wenhui Yu and Zheng Qin. 2020. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. In International Conference on Machine Learning (ICML). 10936--10945.

[41]

Wenhui Yu and Zheng Qin. 2020. Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 861--870.

Digital Library

Cited By

Liu WLiang SZhu HHou U LYu JLi XYin J(2025)Variational Kernel Density Estimation Recommendation Algorithm for Users with Diverse Activity LevelsDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_1(3-18)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_1
Zhang ZLi ZJin JGao XYang XZhang BXiao L(2025)DeepMIN: Deep Multi-modal Interest Network with Cognitive Learning ModulesDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_14(212-227)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_14
Yuan HZhang FLiu L(2024)Research on Course Recommendation Model Based on MLP and Attention MechanismProceedings of the 2024 International Conference on Intelligent Education and Computer Technology10.1145/3687311.3687330(99-103)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3687311.3687330
Show More Cited By

Index Terms

SimpleX: A Simple and Strong Baseline for Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

Recommendations

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 ...
A framework for diversifying recommendation lists by user interest expansion

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...
Cross-representation mediation of user models

Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

the National Key Research and Development Program of China
the National Natural Science Foundation of China
the RD Program of Shenzhen

Conference

CIKM '21

Sponsor:

CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

Acceptance Rates

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

77
Total Citations
View Citations
1,334
Total Downloads

Downloads (Last 12 months)236
Downloads (Last 6 weeks)21

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Liu WLiang SZhu HHou U LYu JLi XYin J(2025)Variational Kernel Density Estimation Recommendation Algorithm for Users with Diverse Activity LevelsDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_1(3-18)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_1
Zhang ZLi ZJin JGao XYang XZhang BXiao L(2025)DeepMIN: Deep Multi-modal Interest Network with Cognitive Learning ModulesDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_14(212-227)Online publication date: 12-Jan-2025
https://doi.org/10.1007/978-981-97-5555-4_14
Yuan HZhang FLiu L(2024)Research on Course Recommendation Model Based on MLP and Attention MechanismProceedings of the 2024 International Conference on Intelligent Education and Computer Technology10.1145/3687311.3687330(99-103)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1145/3687311.3687330
Zhao YChen RHan QSong HChen L(2024)Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled DataProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688149(247-256)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688149
Li SGuo HTang XTang RHou LLi RZhang R(2024)Embedding Compression in Recommender Systems: A SurveyACM Computing Surveys10.1145/363784156:5(1-21)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3637841
Lin FZhao ZZhu XZhang DShen SLi XXu TZhang SChen EBaeza-Yates RBonchi F(2024)When Box Meets Graph Neural Network in Tag-aware RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671973(1770-1780)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671973
Wu YZhang LMo FZhu TMa WNie JBaeza-Yates RBonchi F(2024)Unifying Graph Convolution and Contrastive Learning in Collaborative FilteringProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671840(3425-3436)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671840
Zhang YWu YHan RSun YZhu YLi XLin WZhuang FAn ZXu YBaeza-Yates RBonchi F(2024)Unified Dual-Intent Translation for Joint Modeling of Search and RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671519(6291-6300)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671519
Wu JWang XGao XChen JFu HQiu T(2024)On the Effectiveness of Sampled Softmax Loss for Item RecommendationACM Transactions on Information Systems10.1145/363706142:4(1-26)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3637061
Zhang WYang LSong ZZou HXu KFang LYu PSerra ESpezzano F(2024)Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679773(3248-3258)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679773
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.

Media

Figures

Other

Tables

View Table of Contents