skip to main content
10.1145/3543873.3584631acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
short-paper

HAPENS: Hardness-Personalized Negative Sampling for Implicit Collaborative Filtering

Published: 30 April 2023 Publication History

Abstract

For training implicit collaborative filtering (ICF) models, hard negative sampling (HNS) has become a state-of-the-art solution for obtaining negative signals from massive uninteracted items. However, selecting appropriate hardness levels for personalized recommendations remains a fundamental, yet underexplored, problem. Previous HNS works have primarily adjusted the hardness level by tuning a single hyperparameter. However, applying the same hardness level to each user is unsuitable due to varying user behavioral characteristics, the quantity and quality of user records, and different consistencies of models’ inductive biases. Moreover, increasing the number of hyperparameters is not practical due to the massive number of users. To address this important and challenging problem, we propose a model-agnostic and practical approach called hardness-personalized negative sampling (HAPENS). HAPENS uses a two-stage approach: in stage one, it trains the ICF model with a customized objective function that optimizes its worst performance on each user’s interacted item set. In stage two, it utilizes these worst performances as personalized hardness levels with a well-designed sampling distribution, and trains the final model with the same architecture. We evaluated HAPENS on the collected Bing advertising dataset and one public dataset, and the comprehensive experimental results demonstrate its robustness and superiority. Moreover, HAPENS has delivered significant benefits to the Bing advertising system. To the best of our knowledge, we are the first to study this important and challenging problem.

References

[1]
Hugo Caselles-Dupré, Florian Lesaint, and Jimena Royo-Letelier. 2018. Word2vec Applied to Recommendation: Hyperparameters Matter. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 352–356. https://doi.org/10.1145/3240323.3240377
[2]
Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the AAAI conference on artificial intelligence, Vol. 29.
[3]
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.
[4]
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.
[5]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, 263–272.
[6]
Jui-Ting Huang, Ashish Sharma, Shuying Sun, Li Xia, David Zhang, Philip Pronin, Janani Padmanabhan, Giuseppe Ottaviano, and Linjun Yang. 2020. Embedding-based retrieval in facebook search. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2553–2561.
[7]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.
[8]
Yehuda Koren, Steffen Rendle, and Robert Bell. 2022. Advances in collaborative filtering. Recommender systems handbook (2022), 91–142.
[9]
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. 689–698.
[10]
Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. 2021. UltraGCN: ultra simplification of graph convolutional networks for recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1253–1262.
[11]
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller. 1953. Equation of state calculations by fast computing machines. The journal of chemical physics 21, 6 (1953), 1087–1092.
[12]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and Their Compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2(NIPS’13). Curran Associates Inc., Red Hook, NY, USA, 3111–3119.
[13]
Lorenzo Minto, Moritz Haller, Benjamin Livshits, and Hamed Haddadi. 2021. Stronger privacy for federated collaborative filtering with implicit feedback. In Fifteenth ACM Conference on Recommender Systems. 342–350.
[14]
Dae Hoon Park and Yi Chang. 2019. Adversarial sampling and training for semi-supervised information retrieval. In The World Wide Web Conference. 1443–1453.
[15]
Yuqi Qin, Pengfei Wang, and Chenliang Li. 2021. The world is binary: Contrastive learning for denoising next basket recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 859–868.
[16]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).
[17]
Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009 (2009).
[18]
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 515–524.
[19]
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021. Denoising implicit feedback for recommendation. In Proceedings of the 14th ACM international conference on web search and data mining. 373–381.
[20]
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.
[21]
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 2020. 441–447.
[22]
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang. 2020. Understanding negative sampling in graph representation learning. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 1666–1676.
[23]
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.
[24]
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 785–788.
[25]
Tong Zhao, Julian McAuley, and Irwin King. 2015. Improving latent factor models via personalized feature projection for one class recommendation. In Proceedings of the 24th ACM international on conference on information and knowledge management. 821–830.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
April 2023
1567 pages
ISBN:9781450394192
DOI:10.1145/3543873
This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Check for updates

Author Tags

  1. Collaborative Filtering
  2. Negative Sampling;
  3. Personalization

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

WWW '23
Sponsor:
WWW '23: The ACM Web Conference 2023
April 30 - May 4, 2023
TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 136
    Total Downloads
  • Downloads (Last 12 months)36
  • Downloads (Last 6 weeks)2
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media