TF-LLMER resolves optimization barriers in LLM-enhanced recommenders through embedding normalization and Rec-PCA that aligns semantic representations with collaborative co-occurrence graphs.
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , pages=
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RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.
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Break the Optimization Barrier of LLM-Enhanced Recommenders: A Theoretical Analysis and Practical Framework
TF-LLMER resolves optimization barriers in LLM-enhanced recommenders through embedding normalization and Rec-PCA that aligns semantic representations with collaborative co-occurrence graphs.
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Robust Recommendation from Noisy Implicit Feedback: A GMM-Weighted Bayes-label Transition Matrix Framework
RGBT combines GMM-derived instance reliability weights with a Bayes-label transition matrix to achieve consistent, low-variance estimation from noisy implicit feedback while using all samples.