Collaborative filtering and deep learning based hybrid recommendation for cold start problem


Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences > Adaptive communications networks research group
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Event Title: 2016 IEEE Cyber Science and Technology Congress / 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing / 2016 IEEE 14th International Conference on Pervasive Intelligence and Computing / 2016 IEEE 2nd International Confere
Event Type: Other
Event Dates: 2016-08-08 - 2016-08-10
Uncontrolled Keywords: collaboration,computational modeling,data models,machine learning,motion pictures,predictive models,training,Computer Vision and Pattern Recognition,Information Systems,Computer Science (miscellaneous),Artificial Intelligence,Computer Networks and Communications
ISBN: 978-1-5090-4065-0
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-10-11
Accepted Date: 2016-08-01
Authors: Wei, Jian
He, Jianhua (ORCID Profile 0000-0002-5738-8507)
Chen, Kai
Zhou, Yi
Tang, Zuoyin (ORCID Profile 0000-0001-7094-999X)



Version: Accepted Version

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