GitHub Repository Maps Generative AI Research and Practice for Search and Recommendation

Summary

The GitHub repository is a curated compendium of research contributions and industrial engineering practices that use generative AI and large language models to build search, recommender, personalization, and question-answering systems. It organizes surveys, conference and workshop materials, tutorials, software libraries, frameworks, blog posts, and white papers. Major sections cover agentic and conversational search, search assistance, personalization, multimodal and multilingual retrieval, structured-data querying, RAG and GraphRAG, ranking and reranking, embeddings, query understanding, document understanding, response generation, and deep research. The repository also tracks evaluation resources for search engines, RAG and QA systems, deep-research agents, agentic search, reasoning-intensive retrieval, and general QA. Separate sections collect recommender-system methods such as LLM rankers, sequential recommendation, discovery, and industrial approaches. Applications are grouped into areas including product search, maps and travel, advertising, healthcare, science, finance, legal work, jobs, and manufacturing maintenance. The collection includes both academic references and practical tools such as retrieval, reranking, embedding, and deep-research frameworks, making it a broad starting point for researchers and engineers exploring LLM-based information access systems.