Efficient Thompson sampling for online matrix-factorization recommendation
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20670873
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1
- First page
- 1297
- Volume
- 1
- Issue
- -
- ISSN
- 1049-5258
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper published at the top machine learning conference is the state of the art in the domain of Bayesian matrix factorisation based recommendation. It formed the basis for Project Izar, an internal project at Adobe Research. For more details of this internal project, please contact: Hung Bui (now with Google Deepmind and VinAI - bui.h.hung@gmail.com), and Branislav Kveton (now with Google Research - bkveton@google.com).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -