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Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization

Published: 08 October 2024 Publication History

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.

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  • (2024)Stalactite: toolbox for fast prototyping of vertical federated learning systemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691700(1187-1190)Online publication date: 8-Oct-2024
  • (2024)Federated cross-domain recommendation system based on bias eliminator and personalized extractorKnowledge and Information Systems10.1007/s10115-024-02316-y67:3(2935-2965)Online publication date: 30-Dec-2024

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
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Published: 08 October 2024

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  1. Alternating Direction Method of Multipliers (ADMM)
  2. Alternating Least Squares (ALS)
  3. Cross-Domain Recommender System (CDRS)
  4. Implicit Matrix Factorization

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  • (2024)Stalactite: toolbox for fast prototyping of vertical federated learning systemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691700(1187-1190)Online publication date: 8-Oct-2024
  • (2024)Federated cross-domain recommendation system based on bias eliminator and personalized extractorKnowledge and Information Systems10.1007/s10115-024-02316-y67:3(2935-2965)Online publication date: 30-Dec-2024

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