#Recommender Systems

An extensive collection of papers and tools showcases the integration of large language models (LLMs) in recommender systems, covering aspects from feature engineering to personalized recommendations. The project features a survey paper accepted by ACM Transactions on Information Systems, which delves into the latest advancements in LLM-enhanced recommender systems. Regular updates ensure it remains a comprehensive resource for understanding the synergy between LLMs and recommendation technologies.

The QRec framework, built for Python and Tensorflow applications, offers cross-platform support, rapid execution, and straightforward configuration. It enables the implementation and assessment of advanced recommendation models via an intuitive user interface. QRec facilitates expansion by integrating new algorithms and supports both memory-based and model-based methods. The framework is regularly updated with new advancements such as SimGCL and BUIR, ensuring optimal operation on Windows, Linux, and Mac OS, making it an essential tool for data scientists and AI researchers.

This resource provides a thorough overview of key SaaS, open-source, and academic recommender systems. It details various platforms offering personalized recommendations, describes their features and considerations, and serves as a neutral guide for those interested in understanding or developing recommendation engines.