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


Autoria(s): Wei, Jian; He, Jianhua; Chen, Kai; Zhou, Yi; Tang, Zuoyin
Contribuinte(s)

Bilof, Randall

Data(s)

11/10/2016

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.aston.ac.uk/29586/1/Recommendation_system_for_cold_start_items.pdf

Wei, Jian; He, Jianhua; Chen, Kai; Zhou, Yi and Tang, Zuoyin (2016). Collaborative filtering and deep learning based hybrid recommendation for cold start problem. IN: Proceedings - 2016 IEEE Cyber Science and Technology Congress (CyberSciTech 2016), 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing (DASC 2016), 2016 IEEE 14th International Conference on Pervasive Intelligence and Com. Bilof, Randall (ed.) Piscataway, NJ (US): IEEE.

Publicador

IEEE

Relação

http://eprints.aston.ac.uk/29586/

Tipo

Book Section

NonPeerReviewed