Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910617963593728 |
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| author | Samra, Abdulaziz Frolov, Evgeney Vasilev, Alexey Grigorievskiy, Alexander Vakhrushev, Anton |
| author_facet | Samra, Abdulaziz Frolov, Evgeney Vasilev, Alexey Grigorievskiy, Alexander Vakhrushev, Anton |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15568 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization Samra, Abdulaziz Frolov, Evgeney Vasilev, Alexey Grigorievskiy, Alexander Vakhrushev, Anton Information Retrieval Machine Learning 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. |
| title | Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2409.15568 |