westlake-repl/Recommendation-Systems-without-Explicit-ID-Features-A-Literature-Review
Paper List of Pre-trained Foundation Recommender Models
This is a curated list of research papers and datasets focused on advanced recommendation systems. It explores how large language models (LLMs) and multimodal data can create more sophisticated recommendations without relying heavily on traditional user ID features. Researchers and data scientists who are building or improving recommendation engines would use this resource to stay updated on cutting-edge techniques and find relevant datasets.
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Use this if you are a researcher or data scientist investigating how foundation models, large language models, or multimodal data can be applied to build next-generation recommendation systems.
Not ideal if you are looking for an off-the-shelf software library or a practical guide to implement basic recommendation algorithms.
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Aug 12, 2024
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