Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems.
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Massie_2
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2014.11.004
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 25
- Volume
- 219
- Issue
- -
- ISSN
- 1872-7921
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- 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
-
-
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel approach to building hybrid recommender systems by injecting content-based representations was further refined, with “Music recommendation: Audio neighbourhoods to discover music in the long tail” winning best paper award at ICCBR_2015 (https://www.agcc.co.uk/news-article/rgu-researchers-transform-online-music-discovery). This work is significant in ongoing research activity on content-based recommendation with text including “Harnessing background knowledge for e-learning recommendation” winning the Donald Michie Memorial Award for best technical paper at BCS AI conference in 2016 (http://www.conferenceexpert.org.uk/admin/papers2.php?conf=ai2016&f=f3). Other related publications include “An e-Learning Recommender that Helps Learners Find the Right Materials” in AAAI (2018) and “Improving e-learning Recommendation using Background Knowledge” in Expert Systems Journal (2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
