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Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
(ACM|18th ACM Conference on Recommender Systems, 2024-10-08)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 ... -
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
(ACM|18th ACM Conference on Recommender Systems, 2024-10-08)Scalability issue plays a crucial role in productionizing modern recommender systems. Even lightweight architectures may suffer from high computational overload due to intermediate calculations, limiting their practicality ... -
From Variability to Stability: Advancing RecSys Benchmarking Practices
(ACM|Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024-08-25)In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach ...
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