The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
Abstract
Recommender systems based on collaborative filtering predict user preferences for products or services by learning past user-item relationships. A predominant approach to collaborative filtering is neighborhood based ("k-nearest neighbors"), where a user-item preference rating is interpolated from ratings of similar items and/or users. We enhance the neighborhood-based approach leading to substantial improvement of prediction accuracy, without a meaningful increase in running time. First, we remove certain so-called "global effects" from the data to make the ratings more comparable, thereby improving interpolation accuracy. Second, we show how to simultaneously derive interpolation weights for all nearest neighbors, unlike previous approaches where each weight is computed separately. By globally solving a suitable optimization problem, this simultaneous interpolation accounts for the many interactions between neighbors leading to improved accuracy. Our method is very fast in practice, generating a prediction in about 0.2 milliseconds. Importantly, it does not require training many parameters or a lengthy preprocessing, making it very practical for large scale applications. Finally, we show how to apply these methods to the perceivably much slower user-oriented approach. To this end, we suggest a novel scheme for low dimensional embedding of the users. We evaluate these methods on the netflix dataset, where they deliver significantly better results than the commercial netflix cinematch recommender system.
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
(2021) · A First Course in Statistical Programming with R · doi:10.1017/9781108993456.011 · Cited by 13,724
Adomavicius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,310
(1999) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/b98874 · Cited by 9,482
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992) · Communications of the ACM · doi:10.1145/138859.138867 · Cited by 4,066
An Algorithmic Framework for Performing Collaborative Filtering
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130372 · Cited by 2,512
GroupLens: applying collaborative filtering to Usenet news
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997) · Communications of the ACM · doi:10.1145/245108.245126 · Cited by 2,367
An algorithmic framework for performing collaborative filtering
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999) · Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/312624.312682 · Cited by 2,011
Cited by
Lessons from the Netflix prize challenge
Bell, R. M., & Koren, Y. (2007) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1345448.1345465 · Cited by 727
Self-citationAdvances in Collaborative Filtering
Koren, Y., Rendle, S., & Bell, R. (2022) · Recommender Systems Handbook · doi:10.1007/978-1-0716-2197-4_3 · Cited by 324
Self-citationComputer-aided Diagnosis: How to Move from the Laboratory to the Clinic
van Ginneken, B., Schaefer-Prokop, C. M., & Prokop, M. (2011) · Radiology · doi:10.1148/radiol.11091710 · Cited by 306
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
Noulas, A., Scellato, S., Lathia, N., & Mascolo, C. (2012) · 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing · doi:10.1109/socialcom-passat.2012.70 · Cited by 184
All Together Now: A Perspective on the Netflix Prize
Bell, R. M., Koren, Y., & Volinsky, C. (2010) · CHANCE · doi:10.1080/09332480.2010.10739787 · Cited by 91
Self-citation
Metadata
- Authors
- Bell, R. M., & Koren, Y.
- Venue
- Seventh IEEE International Conference on Data Mining (ICDM 2007)
- Publisher
- IEEE
- Published
- 2007-10
- Pages
- 43-52
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2172249709
- Cited by
- 499
- References
- 19
- FWCI
- 21.57
- 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: Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. https://app.cochat.ai/papers/10.1109/icdm.2007.90
- The paper
- Bell, R. M., & Koren, Y. (2007). Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. Seventh IEEE International Conference on Data Mining (ICDM 2007). https://doi.org/10.1109/icdm.2007.90
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.
