Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
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.
Cite
@article{arxiv.2409.15568,
title = {Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization},
author = {Abdulaziz Samra and Evgeney Frolov and Alexey Vasilev and Alexander Grigorievskiy and Anton Vakhrushev},
journal= {arXiv preprint arXiv:2409.15568},
year = {2024}
}