Learning to Diversify Web Search Results with a Document Repulsion Model
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1460273
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2017.05.027
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 136
- Volume
- 411
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2017
- 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
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper, published in Information Sciences, proposes a principled diversity search approach. It adapts the learning-to-rank framework by directly optimizing the search diversification performance as an objective function. This overcomes the limitations of existing methods that often heuristically balance the relevance and diversity scores. The proposed model significantly outperforms state-of-the-art approaches in terms of effectiveness, robustness and efficiency. The paper’s significance is evidenced by its theoretical component having been adopted as a key building block by (Du et al., 2018), and being identified as a representative search diversification model in (Wu et al., 2019) and (Kang et al., 2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -