The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Disentangled Graph Collaborative Filtering
Open accessAbstract
Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401944 · Cited by 3,852
Neural Graph Collaborative Filtering
Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331267 · Cited by 3,823
Self-citationGraph Convolutional Neural Networks for Web-Scale Recommender Systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219890 · Cited by 3,733
(2017) · International Conference on Learning Representations · Cited by 3,248
KGAT: Knowledge Graph Attention Network for Recommendation
Wang, X., He, X., Cao, Y., Liu, M., & Chua, T.-S. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330989 · Cited by 2,147
Self-citationRestricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Variational Autoencoders for Collaborative Filtering
Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186150 · Cited by 1,338
AutoRec: Autoencoders Meet Collaborative Filtering
Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2740908.2742726 · Cited by 1,175
Cited by
Disentangled Contrastive Collaborative Filtering
Ren, X., Xia, L., Zhao, J., Yin, D., & Huang, C. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591665 · Cited by 171
Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zhao, Z., Chen, J., Zhou, S., He, X., Cao, X., Zhang, F., & Wu, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3218994 · Cited by 117
Self-citationDecentralized Graph Neural Network for Privacy-Preserving Recommendation
Zheng, X., Wang, Z., Chen, C., Qian, J., & Yang, Y. (2023) · Proceedings of the 32nd ACM International Conference on Information and Knowledge Management · doi:10.1145/3583780.3614834 · Cited by 10
Metadata
- Authors
- Wang, X., Jin, H., Zhang, A., He, X., Xu, T., & Chua, T.-S.
- Venue
- Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2020-07-25
- Pages
- 1001-1010
- OpenAlex
- W3044311607
- Cited by
- 624
- References
- 18
- FWCI
- 51.40
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Disentangled Graph Collaborative Filtering. https://app.cochat.ai/papers/10.1145/3397271.3401137
- The paper
- Wang, X., Jin, H., Zhang, A., He, X., Xu, T., & Chua, T.-S. (2020). Disentangled Graph Collaborative Filtering. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3397271.3401137
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
