HybridSVD: When collaborative information is not enough

Evgeny Frolov, Ivan Oseledets

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

    13 Citations (Scopus)


    We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique. One of its key features is that it naturally extends a simple PureSVD approach and inherits its unique advantages, such as highly efficient Lanczos-based optimization procedure, simplified hyper-parameter tuning and a quick folding-in computation for generating recommendations instantly even in highly dynamic online environments. The algorithm utilizes a generalized formulation of the singular value decomposition, which adds flexibility to the solution and allows imposing the desired structure on its latent space. Conveniently, the resulting model also admits an efficient and straightforward solution for the cold start scenario. We evaluate our approach on a diverse set of datasets and show its superiority over similar classes of hybrid models.

    Original languageEnglish
    Title of host publicationRecSys 2019 - 13th ACM Conference on Recommender Systems
    PublisherAssociation for Computing Machinery, Inc
    Number of pages9
    ISBN (Electronic)9781450362436
    Publication statusPublished - 10 Sep 2019
    Event13th ACM Conference on Recommender Systems, RecSys 2019 - Copenhagen, Denmark
    Duration: 16 Sep 201920 Sep 2019

    Publication series

    NameRecSys 2019 - 13th ACM Conference on Recommender Systems


    Conference13th ACM Conference on Recommender Systems, RecSys 2019


    • Cold Start
    • Collaborative Filtering
    • Hybrid Recommenders
    • Matrix Factorization
    • PureSVD
    • Top-N Recommendation


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