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

Jian Wei, Jianhua He, Kai Chen (Lead / Corresponding author), Yi Zhou, Zuoyin Tang

    Research output: Chapter in Book/Report/Conference proceedingChapter

    19 Citations (Scopus)
    190 Downloads (Pure)

    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 Com
    EditorsRandall Bilof
    Place of PublicationPiscataway
    PublisherIEEE
    Pages874-877
    Number of pages4
    ISBN (Electronic)9781509040650
    ISBN (Print)9781509040667
    DOIs
    Publication statusPublished - 13 Oct 2016

    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

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