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Jul, 2023
基于 Fisher 加权的对比学习模型在顺序推荐中的合并
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation
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Jung Hyun Ryu, Jaeheyoung Jeon, Jewoong Cho, Myungjoo Kang 1
TL;DR
本研究应用 Fisher-Merging 方法解决推荐系统中动态偏好的问题，提高了系统整体性能，为序列学习和推荐系统的发展提供了潜力。
Abstract
Along with the exponential growth of online platforms and services,
recommendation systems
have become essential for identifying relevant items based on user preferences. The domain of
sequential recommendation
a
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