Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
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3640457.3688143.pdf
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722.35 KB
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Author(s)
Samra, Abdulaziz
Frolov, Evgeny
Vasilev, Alexey
Grigorevskiy, Alexander
Vakhrushev, Anton
Date Issued
October 8, 2024
Publisher
ACM|18th ACM Conference on Recommender Systems
Citation
Samra, Abdulaziz, Frolov, Evgeny, Vasilev, Alexey, Grigorevskiy, Alexander and Vakhrushev, Anton. 2024. "Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization."
Version
Final published version
Abstract
Data sparsity has been one of the long-standing problems for recommender systems. One of the solutions to mitigate this issue is to exploit knowledge available in other source domains. However, many cross-domain recommender systems introduce a complex architecture that makes them less scalable in practice. On the other hand, matrix factorization methods are still considered to be strong baselines for single-domain recommendations. In this paper, we introduce the CDIMF, a model that extends the standard implicit matrix factorization with ALS to cross-domain scenarios. We apply the Alternating Direction Method of Multipliers to learn shared latent factors for overlapped users while factorizing the interaction matrix. In a dual-domain setting, experiments on industrial datasets demonstrate a competing performance of CDIMF for both cold-start and warm-start. The proposed model can outperform most other recent cross-domain and single-domain models. We also provide the code to reproduce experiments on GitHub.
Description
RecSys ’24, October 14–18, 2024, Bari, Italy
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3640457.3688143
