It is a recommender system simulator called Agent4Rec, designed to explore the potential of large language model (LLM)-empowered generative agents in simulating human-like behavior in recommendation environments.
It is a recommender system simulator called Agent4Rec, designed to explore the potential of large language model (LLM)-empowered generative agents in simulating human-like behavior in recommendation environments. The system uses 1,000 generative agents initialized from the MovieLens-1M dataset, each embodying unique social traits and preferences. These agents interact with personalized movie recommendations in a page-by-page manner, performing actions such as watching, rating, evaluating, exiting, and interviewing. Agent4Rec aims to simulate the behavior of independent humans in recommendation systems, providing insights into how generative agents can enhance such environments.
To set up Agent4Rec, users must create a virtual environment, manually install PyTorch, and install dependencies listed in the requirements.txt file. The system has been tested on Python 3.9.12 with PyTorch 1.13.1+cu117, and using Python versions above 3.10 may cause bugs in the ‘reckit’ package. Users must also export their OpenAI API key to power the simulation, which relies on ChatGPT-3.5. A toy simulation with three agents can be run to observe agent responses to recommendations, taking approximately three minutes to complete.
Agent4Rec supports various recommendation systems and simulation configurations. For example, users can run a simulation named “MyExp” with 10 agents, each browsing up to five pages with four items per page, using the Matrix Factorization (MF) recommender. The simulation can be executed in parallel to speed up the process. Results are saved in a designated directory, with interaction histories logged for analysis. A full simulation involving 1,000 agents costs approximately $16, or $0.016 per user.
The system is part of the implementation of the paper “On Generative Agents in Recommendation,” presented at SIGIR 2024. It provides a platform for experimenting with different recommender models and configurations, offering valuable insights into the behavior of generative agents in recommendation systems.
It is a 124-billion-parameter open-weights multimodal model called Pixtral Large, built on Mistral Large 2, designed to excel in both image and text understanding.
It is a platform designed to build and deploy AI agents that address trust barriers in adopting agentic AI by embedding data protection, policy enforcement, and validation into every agent, ensuring business success.
It is an open-source framework designed to provide AI Agents with reliable memory capabilities for decision-making, personalized goal setting, and execution in AI applications.
It is a platform designed to enable developers to build, deploy, and monetize AI Agents while providing a digital marketplace called the Agent Hub for users to access and utilize these agents.
It is an autonomous framework designed for data labeling and processing tasks, enabling the creation of intelligent agents that can independently learn and apply skills through iterative processes.
It is a framework designed to unify and optimize human-designed prompt engineering techniques for improving problem-solving capabilities of Large Language Models (LLMs) by representing LLM-based agents as computational graphs.
It is a comprehensive platform and suite of tools designed to provide high-quality data and solutions for training, fine-tuning, and evaluating AI models, particularly for generative AI, government, and enterprise applications.
It is a preliminary implementation of the paper "Improving Factuality and Reasoning in Language Models through Multiagent Debate," which aims to enhance the accuracy and reasoning capabilities of language models by employing a multiagent debate framework.
It is an implementation of "Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4," a project designed to develop an AI agent capable of playing imperfect information games using GPT-4 enhanced with Theory of Mind (ToM) awareness.
It is an open-source LLMOps platform called Agenta that provides integrated tools for prompt engineering, versioning, evaluation, and observability, all in one place.
It is a 124-billion-parameter open-weights multimodal model called Pixtral Large, built on Mistral Large 2, designed to excel in both image and text understanding.
It is a platform designed to simplify AI integration for developers by providing ready-to-use, copy-paste code snippets that can be seamlessly incorporated into projects.
It is a customer support AI agent and platform designed to provide easy, secure, and scalable 24/7 multilingual support across voice, chat, WhatsApp, and email.
It is an AI-powered meeting assistant designed to enhance productivity by providing transcription, summarization, and smart organization of meeting content.
