The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy
Abstract
In the past decade large scale recommendation datasets were published and extensively studied. In this work we describe a detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models. The Yahoo! Music dataset consists of more than a million users, 600 thousand musical items and more than 250 million ratings, collected over a decade. It is characterized by three unique features: First, rated items are multi-typed, including tracks, albums, artists and genres; Second, items are arranged within a four level taxonomy, proving itself effective in coping with a severe sparsity problem that originates from the unusually large number of items (compared to, e.g., movie ratings datasets). Finally, fine resolution timestamps associated with the ratings enable a comprehensive temporal and session analysis. We further present a matrix factorization model exploiting the special characteristics of this dataset. In particular, the model incorporates a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings. To gain additional insights of its properties, we organized the KddCup-2011 competition about this dataset. As the competition drew thousands of participants, we expect the dataset to attract considerable research activity in the future.
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
A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Self-citationConvergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
Lagarias, J. C., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998) · SIAM Journal on Optimization · doi:10.1137/s1052623496303470 · Cited by 7,447
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
Self-citation(2011) · Springer eBooks · doi:10.1007/978-0-387-85820-3 · Cited by 3,761
Moran, P. A. P., & Kendall, M. G. (1951) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2980903 · Cited by 2,573
Methods and metrics for cold-start recommendations
Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002) · Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/564376.564421 · Cited by 1,780
Collaborative 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
Self-citationBennett, J. R., & Lanning, S. (2007) · Cited by 1,134
Govindarajulu, Z., Kendall, M., & Gibbons, J. D. (1992) · Technometrics · doi:10.2307/1269571 · Cited by 1,130
Cited by
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
He, R., & McAuley, J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9973 · Cited by 992
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
Campos, P. G., Díez, F., & Cantador, I. (2014) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-012-9136-x · Cited by 437
Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution
Günnemann, S., Günnemann, N., & Faloutsos, C. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623721 · Cited by 47
Metadata
- Authors
- Koenigstein, N., Dror, G., & Koren, Y.
- Venue
- Proceedings of the fifth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2011-10-23
- Pages
- 165-172
- OpenAlex
- W2041022395
- Cited by
- 273
- References
- 24
- FWCI
- 17.07
- 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: Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. https://app.cochat.ai/papers/10.1145/2043932.2043964
- The paper
- Koenigstein, N., Dror, G., & Koren, Y. (2011). Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. Proceedings of the fifth ACM conference on Recommender systems. https://doi.org/10.1145/2043932.2043964
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.
