Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13
- Type
- D - Journal article
- DOI
-
10.1016/j.eswa.2013.11.010
- Title of journal
- Expert Systems with Applications
- Article number
- -
- First page
- 3261
- Volume
- 41
- Issue
- 7
- ISSN
- 0957-4174
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S0957417413009214
- 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
-
1
- Research group(s)
-
1 - Intelligent Systems
- Citation count
- 54
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper focuses on gray-sheep users in recommender systems (RS). Most e-commerce websites work by finding similar users and making recommendations based on their opinion. However, certain users have unique taste and they would not benefit from the traditional approach. This paper proposed a new way to cluster gray-sheep users and uses machine learning algorithms to tailor recommendations based on their unique taste. The business side/ROI of this paper is huge as it is estimated that up to 50% of e-commerce services’ (e.g. Netflix, Amazon) revenue is generated by their Recommender Systems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -