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

Abstract

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: https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.149
Divisions: College of Engineering & Physical Sciences > Adaptive communications networks research group
Additional Information: -© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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
Last Modified: 30 Sep 2024 09:16
Date Deposited: 06 Dec 2016 11:30
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)

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Version: Accepted Version


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