A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation
원문 초록
This study aims at comparing two sequential recommender systems: Self-Attention based Sequential Recommendation (SASRec), and Beyond Self-Attention based Sequential Recommendation (BSARec) in order to check the improvement frequency enhancement - the added element in BSARec - has on recommendations. The models in the study, have been re-implemented with a common base-structure from EasyRec, with the aim of obtaining a fair and reproducible comparison. The results obtained displayed how BSARec, by including bias terms for frequency enhancement, does indeed outperform SASRec, although the increases in performance obtained, are not as high as those presented by the authors. This work aims at offering an overview on existing methods, and most importantly at underlying the importance of implementation details for performance comparison.
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RecGPT: A Foundation Model for Sequential Recommendation
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
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