#Personalization

Mem0 offers an innovative memory layer for AI, enhancing personalization by adapting to user needs. Ideal for AI assistants, chatbots, and autonomous systems, this open-source solution uses a hybrid database for efficient memory management. Key features include multi-level memory retention and a user-friendly API. Easily integrate Mem0 to optimize memory retrieval and ensure context-aware interactions. Choose from managed services or self-host with simple pip installation, supporting Graph Memory and various LLMs.

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.

The repository provides a wide range of resources and studies focused on using large language models for character role-playing. It includes various fictional and cultural figures, with insights into applications in multi-agent systems and personalization. Updates on benchmarks, relevant research, and field contributions are featured, highlighting the relevance of role-playing language agents. Explore the latest research and tools examining the psychology of language models, including biases and the changing dynamics of role-play with predetermined personas.