CoChat
Sign Up
Cited by 369Recommender Systems and TechniquesInformation Retrieval and Search BehaviorAdvanced Bandit Algorithms Research

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

Open access
Zheng, Yu, Gao, Chen, Li, Xiang, He, Xiangnan, Li, Yong, Jin, Depeng·2021·Proceedings of the Web Conference 2021
DOI 10.1145/3442381.3449788
Open this paper in CoChat — free account

Abstract

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, e.g., by re-weighting training samples or leveraging a small fraction of unbiased data. However, the variety of user conformity is ignored by these approaches, and different causes of an interaction are bundled together as unified representations, hence robustness and interpretability are not guaranteed when underlying causes are changing. In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled, and various backbone recommendation models could be smoothly integrated. We assign users and items with separate embeddings for interest and conformity, and make each embedding capture only one cause by training with cause-specific data which is obtained according to the colliding effect of causal inference. Our proposed methodology outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets on top of various backbone models. We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.

Overview

Integrity signals

Registry

Crossref · 10.1145/3442381.3449788

Cites

35 of 35 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 34 references

Research works printed but not linked: 15

  1. Robert M Bell , Yehuda Koren , and Chris Volinsky . 2008. The bellkor 2008 solution to the netflix prize. Statistics Research Department at AT&T Research 1 ( 2008 ). Robert M Bell, Yehuda Koren, and Chris Volinsky. 2008. The bellkor 2008 solution to the netflix prize. Statistics Research Department at AT&T Research 1 (2008).
  2. James Bennett , Stan Lanning , 2007 . The netflix prize . In Proceedings of KDD cup and workshop, Vol. 2007 . Citeseer, 35. James Bennett, Stan Lanning, 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol. 2007. Citeseer, 35.
  3. Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv preprint arXiv:2010.03240(2020). Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv preprint arXiv:2010.03240(2020). · arXiv
  4. Spyros Gidaris Praveer Singh and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728(2018). Spyros Gidaris Praveer Singh and Nikos Komodakis. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728(2018). · arXiv
  5. Irina Higgins , Loic Matthey , Arka Pal , Christopher Burgess , Xavier Glorot , Matthew Botvinick , Shakir Mohamed , and Alexander Lerchner . 2017. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.Iclr 2, 5 ( 2017 ), 6. Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.Iclr 2, 5 (2017), 6.
  6. Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014). · arXiv
  7. Dawen Liang , Laurent Charlin , and David M Blei . 2016 . Causal inference for recommendation. In Causation: Foundation to Application , Workshop at UAI. AUAI. Dawen Liang, Laurent Charlin, and David M Blei. 2016. Causal inference for recommendation. In Causation: Foundation to Application, Workshop at UAI. AUAI.
  8. Francesco Locatello , Stefan Bauer , Mario Lucic , Gunnar Raetsch , Sylvain Gelly , Bernhard Schölkopf , and Olivier Bachem . 2019 . Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. In International Conference on Machine Learning. 4114–4124 . Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. 2019. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. In International Conference on Machine Learning. 4114–4124.
  9. Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. In NeurIPS. 5712–5723. Jianxin Ma Chang Zhou Peng Cui Hongxia Yang and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. In NeurIPS. 5712–5723.
  10. Benjamin Marlin Richard S Zemel Sam Roweis and Malcolm Slaney. 2012. Collaborative filtering and the missing at random assumption. arXiv preprint arXiv:1206.5267(2012). Benjamin Marlin Richard S Zemel Sam Roweis and Malcolm Slaney. 2012. Collaborative filtering and the missing at random assumption. arXiv preprint arXiv:1206.5267(2012). · arXiv
  11. Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G. Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR abs/1906.00091(2019). https://arxiv.org/abs/1906.00091 Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G. Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep Learning Recommendation Model for Personalization and Recommendation Systems. CoRR abs/1906.00091(2019). https://arxiv.org/abs/1906.00091 · arXiv
  12. Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2012 . BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012). Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012). · arXiv
  13. Bernhard Schölkopf. 2019. Causality for Machine Learning. arXiv preprint arXiv:1911.10500(2019). Bernhard Schölkopf. 2019. Causality for Machine Learning. arXiv preprint arXiv:1911.10500(2019). · arXiv
  14. Gábor J Székely , Maria L Rizzo , Nail K Bakirov , 2007. Measuring and testing dependence by correlation of distances. The annals of statistics 35, 6 ( 2007 ), 2769–2794. Gábor J Székely, Maria L Rizzo, Nail K Bakirov, 2007. Measuring and testing dependence by correlation of distances. The annals of statistics 35, 6 (2007), 2769–2794.
  15. Xiang Wang , Hongye Jin , An Zhang , Xiangnan He , Tonog Xu , and Tat-Seng Chua . 2020. Disentagnled Graph Collaborative Filtering. SIGIR ( 2020 ). Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tonog Xu, and Tat-Seng Chua. 2020. Disentagnled Graph Collaborative Filtering. SIGIR (2020).

Cited by

Sorted by citations

Items 201–300 of 369

Related

See all

Metadata

Authors
Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D.
Affiliations
  • Beijing National Research Center for Information Science and Technology (CN)
  • University of Hong Kong (HK)
  • University of Science and Technology of China (CN)
ORCID
Venue
Proceedings of the Web Conference 2021
Publisher
ACM
Published
2021-04-19
Pages
2980-2991
DOI
10.1145/3442381.3449788
OpenAlex
W3156622960
Cited by
356
References
34
Citations by year
2026: 32 · 2025: 78 · 2024: 94 · 2023: 92 · 2022: 51 · 2021: 9
FWCI
51.00
Type
conference paper
Language
en
Open access
Licence
Locations
  • International World Wide Web Conference (WWW) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (U19A2079, 61971267, 61972223, 61861136003, U1936217, 61941117)
  • National Key Research and Development Program of China (2020AAA0106000)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Keywords
recommender systems, popularity bias, disentangled representations, causal inference, interpretability, robustness, user interest modeling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review