Collaborative filtering and deep learning based recommendation system for cold start items
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1415
- Type
- D - Journal article
- DOI
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10.1016/j.eswa.2016.09.040
- Title of journal
- Expert Systems with Applications
- Article number
- -
- First page
- 29
- Volume
- 69
- Issue
- -
- ISSN
- 0957-4174
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2016
- 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
-
4
- Research group(s)
-
C - Communications and Networking (Comms)
- Citation count
- 192
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This extremely highly cited paper introduced innovative deep learning recommendation models. Significance comes from the innovative approach of exploiting deep learning and social networks to tackle cold start item recommendation challenges, and demonstration of its value for recommending cold start products which have not been rated before. It has a wide range of applications, such as online shopping and social networking applications. The work, as one of the earliest in this field, is widely-cited and is in the list of top downloaded papers of this journal. It has been followed by many international research groups (e.g. DOI:10.1109/TPDS.2018.2877363 and DOI:10.3390/computation7020025).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -