Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks

Ivan Maksimov, Rodrigo Rivera-Castro, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    Recommender systems have become an essential instrument in a wide range of industries to personalize the user experience. A significant issue that has captured both researchers' and industry experts' attention is the cold start problem for new items. This work presents a graph neural network recommender system using item hierarchy graphs and a bespoke architecture to handle the cold start case for items. The experimental study on multiple datasets and millions of users and interactions indicates that our method achieves better forecasting quality than the state-of-the-art with a comparable computational time.

    Original languageEnglish
    Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
    EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages5128-5137
    Number of pages10
    ISBN (Electronic)9781728162515
    DOIs
    Publication statusPublished - 10 Dec 2020
    Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
    Duration: 10 Dec 202013 Dec 2020

    Publication series

    NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

    Conference

    Conference8th IEEE International Conference on Big Data, Big Data 2020
    Country/TerritoryUnited States
    CityVirtual, Atlanta
    Period10/12/2013/12/20

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