The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Google news personalization: scalable online collaborative filtering
Abstract
Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several millionusers and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.
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
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
Cited by
A contextual-bandit approach to personalized news article recommendation
Li, L., Chu, W., Langford, J., & Schapire, R. E. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772758 · Cited by 2,562
“. service vendors acquire and maintain a large amount of content in their repository, for instance, for filtering news articles [14] or for the display of advertisements [5].”
BackgroundFilter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992
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
Chen (2010) · doi:10.1145/1753326.1753503 · Cited by 416
Measuring price discrimination and steering on e-commerce web sites
Hannak, A., Soeller, G., Lazer, D., Mislove, A., & Wilson, C. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663744 · Cited by 405
Gil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2024) · Political Communication · doi:10.1080/10584609.2023.2290497 · Cited by 198
Adaptive personalization using social networks
Chung, T. S., Wedel, M., & Rust, R. T. (2016) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-015-0441-x · Cited by 184
Interactive collaborative filtering
Zhao, X., Zhang, W., & Wang, J. (2013) · Proceedings of the 22nd ACM international conference on Information & Knowledge Management · doi:10.1145/2505515.2505690 · Cited by 165
Explainable Recommendation through Attentive Multi-View Learning
Gao, J., Wang, X., Wang, Y., & Xie, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013622 · Cited by 127
LaMP: When Large Language Models Meet Personalization
Salemi, A., Mysore, S., Bendersky, M., & Zamani, H. (2023) · arXiv · doi:10.48550/arxiv.2304.11406 · Cited by 109
Quantifying Biases in Online Information Exposure
Nikolov, D., Lalmas, M., Flammini, A., & Menczer, F. (2019) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24121 · Cited by 96
Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., & Hauff, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287583 · Cited by 92
The Anchoring Effect in Decision-Making with Visual Analytics
Cho, I., Wesslen, R., Karduni, A., Santhanam, S., Shaikh, S., & Dou, W. (2017) · 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2017.8585665 · Cited by 88
Multimodal Emotion Recognition With Temporal and Semantic Consistency
Chen, B., Cao, Q., Hou, M., Zhang, Z., Lu, G., & Zhang, D. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3129331 · Cited by 71
Misztal-Radecka, J., & Indurkhya, B. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102519 · Cited by 29
Ma, S., Wei, Z., Tian, F., Fan, X., Zhang, J., Shen, X., Lin, Z., Huang, J., Měch, R., Samaras, D., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300701 · Cited by 24
Want to scale in centralized systems? Think P2P
Kermarrec, A.-M., & Taïani, F. (2015) · Journal of Internet Services and Applications · doi:10.1186/s13174-015-0029-1 · Cited by 18
The Influence of Media Bias on News Recommender Systems
Ruan, Q., Mac Namee, B., & Dong, R. (2023) · Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3565472.3595619 · Cited by 4
Metadata
- Authors
- Das, A. S., Datar, M., Garg, A., & Rajaram, S.
- Venue
- Proceedings of the 16th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2007-05-08
- Pages
- 271-280
- OpenAlex
- W2123427850
- Cited by
- 1,511
- References
- 1
- FWCI
- 47.50
- 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
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.
