Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
This addresses data sparsity for recommender systems by enabling knowledge sharing across domains, though it is incremental as it builds on existing matrix factorization methods.
The paper tackles data sparsity in recommender systems by extending implicit matrix factorization to cross-domain scenarios, achieving competitive performance on industrial datasets for both cold-start and warm-start users.
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.