Novel centroid selection approaches for KMeans-clustering based recommender systems
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54392550
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2015.03.062
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 156
- Volume
- 320
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- 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
- 82
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is a representative paper from a large body of work on recommender systems consisting of ten published papers (https://tinyurl.com/r2ncrvk). The impact of the work is that it goes beyond the usual task of maximising the accuracy of a recommender, exploring issues such as making recommendations for new users of the system or recommending items that have received few recommendations. This particular paper proposes a novel algorithm that tackle the scaling issues due to huge training datasets.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -