DRprofiling: deep reinforcement user profiling for recommendations in heterogenous information networks
- Submitting institution
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The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 90823
- Type
- D - Journal article
- DOI
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10.1109/TKDE.2020.2998695
- Title of journal
- Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 0
- Volume
- 0
- Issue
- -
- ISSN
- 1558-2191
- Open access status
- Other exception
- Month of publication
- -
- Year of publication
- 2020
- 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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0
- Research group(s)
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9 - DSAI
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this paper is that it advances the state of the art in the field of user profiling and personalisation based on big heterogenous information network data. It is the first paper to apply deep reinforcement learning to profile users in heterogenous information network for recommender systems. This research contributes to facilitating more intelligent and accurate personalisation systems. The proposed approach has been validated on publicly available online community data and can be applied in a wide variety of domains including e-commerce, e-learning, and e-healthcare systems.
- Author contribution statement
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- Non-English
- No
- English abstract
- -