Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
<|reference_start|>Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization: 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.<|reference_end|>
@article{samra2024cross-domain,
    title={Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization},
    author={Abdulaziz Samra, Evgeney Frolov, Alexey Vasilev, Alexander
  Grigorievskiy, Anton Vakhrushev},
    journal={arXiv preprint arXiv:2409.15568},
    year={2024},
    doi={10.1145/3640457.3688143},
    archivePrefix={arXiv},
    eprint={2409.15568},
    primaryClass={cs.IR cs.LG}
}