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Found 2 papers, 1 papers with code
Sequence-level Semantic Representation Fusion for Recommender Systems
The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts.
Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis
As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs.
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