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A contextual-bandit approach to personalized news article recommendation
Open accessAbstract
Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.
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“The problem of parameter selection has recently received considerable attention in the machine learning community in the context of multi-armed bandit problems [76], [77], [78].”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [77]
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Metadata
- Authors
- Li, L., Chu, W., Langford, J., & Schapire, R. E.
- Affiliations
- Yahooo! Labs, Santa Clara, CA, USA
- Yahoo (United States) (US)
- Princeton University (US)
- Venue
- Proceedings of the 19th international conference on World wide web
- Publisher
- ACM
- Published
- 2010-04-26
- Pages
- 661-670
- OpenAlex
- W2112420033
- Cited by
- 2,562
- References
- 90
- Citations by year
- 2026: 197 · 2025: 164 · 2024: 171 · 2023: 212 · 2022: 211 · 2021: 317 · 2020: 312 · 2019: 260 · 2018: 177 · 2017: 119 · 2016: 86 · 2015: 112 · 2014: 83 · 2013: 59 · 2012: 41
- FWCI
- 31.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- personalized news recommendation, contextual bandits, personalized web services, collaborative filtering limitations, scalable learning
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