An evolutionary strategy with machine learning for learning to rank in information retrieval
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1318854
- Type
- D - Journal article
- DOI
-
10.1007/s00500-017-2988-6
- Title of journal
- Soft Computing
- Article number
- -
- First page
- 3171
- Volume
- 22
- Issue
- 10
- ISSN
- 1432-7643
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- 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
-
1
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The ranking of retrieved documents is an essential task in search engines and recommendation systems. As the amount of available information increases and changes continuously, learning to rank according to the user’s queries is challenging. Most methods are based on machine learning alone. Here, the novel combination of evolutionary computation with machine learning and evolving the ranking function makes the learning more adaptive than before. This was demonstrated by rigorous experimentation on some of the benchmark datasets most widely used by the scientific community. This paper opened a new research direction by incorporating optimisation into the learning to rank field.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
