Awesome-LLM-in-Social-Science and Awesome-LLM-for-RecSys
These are ecosystem siblings—both are curated paper collections serving different application domains (social science vs. recommender systems) within the broader LLM research landscape, allowing researchers to explore domain-specific LLM applications rather than compete for the same use case.
About Awesome-LLM-in-Social-Science
ValueByte-AI/Awesome-LLM-in-Social-Science
Awesome papers involving LLMs in Social Science.
This resource compiles an extensive collection of research papers focused on the intersection of Large Language Models (LLMs) and social science. It helps social scientists understand how LLMs can be evaluated, aligned with human values, and used to enhance social science research and tools. The output is a curated list of academic papers, beneficial for researchers, academics, and practitioners in fields like psychology, sociology, and political science.
About Awesome-LLM-for-RecSys
CHIANGEL/Awesome-LLM-for-RecSys
Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.
This resource provides a comprehensive collection of research papers and materials exploring how large language models (LLMs) can enhance recommender systems. It organizes recent advancements in areas like feature engineering, user/item representation, and explanation generation, offering a structured overview of this rapidly evolving field. Researchers and practitioners in recommender systems, particularly those interested in leveraging cutting-edge AI for improved personalization, will find this collection valuable.
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