A switching multi-level method for the long tail recommendation problem
- Submitting institution
-
University of Brighton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 7153659
- Type
- D - Journal article
- DOI
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10.3233/JIFS-179331
- Title of journal
- Journal of Intelligent and Fuzzy Systems
- Article number
- -
- First page
- 7189
- Volume
- 37
- Issue
- 6
- ISSN
- 1064-1246
- Open access status
- Exception within 3 months of publication
- Month of publication
- December
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- 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
-
5
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Designing recommender systems that consider both popular and still-emerging products or services of interest is an ongoing challenge. This paper is significant because it delivers an algorithm that can provide recommendations to users that includes items that are relevant but not yet sufficiently popular to make it through other systems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -