#Recommender System

Explore a curated collection of crucial papers and tutorials on Recommender Systems, covering areas such as deep learning, social and industrial applications, privacy, and large language models. The repository updates regularly, offering insights into advanced research and addressing issues such as cold start and click-through rate prediction, making it a valuable resource for academics and industry professionals.

Discover the role of large language models in transforming recommender systems. This curated list of scholarly papers examines the influence of these models on recommendation technology, focusing on advancements, methodologies, and real-world applications. Covering a wide range of topics from zero-shot recommendations to explainable AI, it offers essential insights into the developing landscape of personalized recommendation strategies. Ideal for researchers, developers, and tech enthusiasts seeking to understand and apply LLMs for sophisticated, user-focused recommendation solutions.

This literature review examines the development of recommender systems with a focus on foundational models that do not rely on explicit ID features. It discusses the potential for these systems to evolve independently, akin to foundational models in natural language processing and computer vision, and the ongoing debate regarding the necessity of ID embeddings. The review further explores how Large Language Models (LLMs) may transform recommender systems by shifting focus from matching to generative paradigms. Additionally, it highlights advancements in multimodal and transferable recommender systems, offering insights from empirical research into universal user representation. This review serves as a comprehensive guide to understanding current trends and future directions in the field of recommender systems.