The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Improving pairwise learning for item recommendation from implicit feedback
Abstract
Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In this work, we show that convergence of such SGD learning algorithms slows down considerably if the item popularity has a tailed distribution. We propose a non-uniform item sampler to overcome this problem. The proposed sampler is context-dependent and oversamples informative pairs to speed up convergence. An efficient implementation with constant amortized runtime costs is developed. Furthermore, it is shown how the proposed learning algorithm can be applied to a large class of recommender models. The properties of the new learning algorithm are studied empirically on two real-world recommender system problems. The experiments indicate that the proposed adaptive sampler improves the state-of-the art learning algorithm largely in convergence without negative effects on prediction quality or iteration runtime.
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
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 15,539
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
Self-citationFactorization 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
Probabilistic Matrix Factorization
Mnih, A., & Salakhutdinov, R. (2007) · neural information processing systems · Cited by 3,798
Collaborative Filtering for Implicit Feedback Datasets
Hu, Y., Koren, Y., & Volinsky, C. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.22 · Cited by 3,170
Factorization Machines with libFM
Rendle, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168771 · Cited by 1,337
Self-citationCollaborative filtering with temporal dynamics
Koren, Y. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557072 · Cited by 1,203
One-Class Collaborative Filtering
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.16 · Cited by 1,015
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)
Adams, R. P., Dahl, G. E., & Murray, I. (2010) · Cited by 966
Cited by
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
He, X., Zhang, H., Kan, M.-Y., & Chua, T.-S. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911489 · Cited by 988
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Modeling User Exposure in Recommendation
Liang, D., Charlin, L., McInerney, J., & Blei, D. M. (2016) · Proceedings of the 25th International Conference on World Wide Web · doi:10.1145/2872427.2883090 · Cited by 362
Contrastive Learning for Cold-Start Recommendation
Wei, Y., Wang, X., Li, Q., Nie, L., Li, Y., Li, X., & Chua, T.-S. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475665 · Cited by 331
A novel Enhanced Collaborative Autoencoder with knowledge distillation for top-N recommender systems
Pan, Y., He, F., & Yu, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.12.025 · Cited by 97
Metadata
- Authors
- Rendle, S., & Freudenthaler, C.
- Venue
- Proceedings of the 7th ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2014-02-24
- Pages
- 273-282
- OpenAlex
- W1976999215
- Cited by
- 360
- References
- 22
- FWCI
- 22.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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: Improving pairwise learning for item recommendation from implicit feedback. https://app.cochat.ai/papers/10.1145/2556195.2556248
- The paper
- Rendle, S., & Freudenthaler, C. (2014). Improving pairwise learning for item recommendation from implicit feedback. Proceedings of the 7th ACM international conference on Web search and data mining. https://doi.org/10.1145/2556195.2556248
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.
