Collaborative filtering and deep learning based recommendation system for cold start items
- Submitting institution
-
Aston University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 21486971
- Type
- D - Journal article
- DOI
-
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)
-
B - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper reports innovative deep learning based recommendation models. The significance of this paper lies in the innovative approach of exploiting deep learning and social networks to tackle cold start item recommendation challenges, and the 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 is in the list of top downloaded and cited 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
- -
