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

Jian Wei, Jianhua He, Kai Chen*, Yi Zhou, Zuoyin Tang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.

Original languageEnglish
Title of host publicationProceedings - 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 Computing (PICom 2016), 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing (DataCom 2016)
Subtitle of host publicationDASC-PICom-DataCom-CyberSciTech 2016
EditorsRandall Bilof
Place of PublicationPiscataway, NJ (US)
PublisherIEEE
Pages874-877
Number of pages4
ISBN (Print)978-1-5090-4065-0
DOIs
Publication statusPublished - 11 Oct 2016
Event2016 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 Conference on Big Data Intelligence and Computing - Auckland, New Zealand
Duration: 8 Aug 201610 Aug 2016

Conference

Conference2016 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 Conference on Big Data Intelligence and Computing
Abbreviated titleDASC-PICom-DataCom-CyberSciTech 2016
CountryNew Zealand
CityAuckland
Period8/08/1610/08/16

Bibliographical note

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Keywords

  • collaboration
  • computational modeling
  • data models
  • machine learning
  • motion pictures
  • predictive models
  • training

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  • Cite this

    Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2016). Collaborative filtering and deep learning based hybrid recommendation for cold start problem. In R. Bilof (Ed.), 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 Computing (PICom 2016), 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing (DataCom 2016): DASC-PICom-DataCom-CyberSciTech 2016 (pp. 874-877). IEEE. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.149, https://doi.org/http://ieeexplore.ieee.org/document/7588947/