metadata
license: mit
README
Introduction
This repository is designed for the AgentRecBench project, which aims to provide a comprehensive benchmark for evaluating the performance of recommendation agents. The code for this project can be found at AgentSocietyChallenge.
Directory Structure
task/
This folder contains three major types of recommendation tasks:
- Classic Tasks: These tasks focus on traditional recommendation scenarios where the goal is to provide accurate recommendations based on historical user-item interactions.
- Evolving Interest Tasks: These tasks are designed to capture the dynamic nature of user preferences over time, requiring agents to adapt to changing interests.
- Cold-Start Tasks: These tasks address the challenge of making recommendations for new users or items with limited interaction history.
dataset
The following three folders contain the datasets for each task, processed and formatted for retrieval by the agent:
- process_data_all: Contains datasets for both classic and cold-start tasks.
- process_data_long: Contains datasets for long-term evolving interest tasks.
- process_data_short: Contains datasets for short-term evolving interest tasks.