Music recommenders: user evaluation without real users?
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Massie_3
- Type
- E - Conference contribution
- DOI
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- Title of conference / published proceedings
- 24th International joint conference on artificial intelligence (IJCAI-15)
- First page
- 1749
- Volume
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- Issue
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- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2015
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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- Research group(s)
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- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel user-free evaluation approach developed here has had impact by application in real-world scenarios. The PhD student (Horsburgh - www.linkedin.com/in/benhorsburgh/) has subsequently employed these techniques for cross product recommendations as Senior Data Scientist with both Tesco and Argos. User-free evaluation has continued as a research theme with this work having significant contribution to a novel evaluation approach developed for text generation in https://doi.org/10.1007/978-3-030-58342-2_18 at ICCBR 2020.
- Author contribution statement
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- Non-English
- No
- English abstract
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