Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations

2025-07-25

AI Summary

To address the high cost and low efficiency of manually constructing categorical features in large-scale recommender systems, this paper proposes a large language model (LLM)-driven multi-agent collaborative framework for automatically extracting high-quality, multivalued categorical features from unstructured text. The method integrates dynamic feedback–guided prompt engineering with an oracle-evaluated closed-loop optimization mechanism, enabling automated feature discovery, generation, and validation. Experiments demonstrate that the approach significantly improves recommender model performance (e.g., +2.3% average AUC gain), accelerates feature construction by 5.8×, and reduces human intervention by over 90%. Its core innovation lies in unifying multi-LLM agent collaboration, dynamic feedback loops, and evaluable automated prompt tuning within a single feature engineering closed loop—establishing a scalable, interpretable, end-to-end paradigm for categorical feature construction in recommender systems.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large language models (LLMs) and their associated agent-based frameworks have significantly advanced automated information extraction, a critical component of modern recommender systems. While these multitask frameworks are widely used in code generation, their application in data-centric research is still largely untapped. This paper presents Agent0, an LLM-driven, agent-based system designed to automate information extraction and feature construction from raw, unstructured text. Categorical features are crucial for large-scale recommender systems but are often expensive to acquire. Agent0 coordinates a group of interacting LLM agents to automatically identify the most valuable text aspects for subsequent tasks (such as models or AutoML pipelines). Beyond its feature engineering capabilities, Agent0 also offers an automated prompt-engineering tuning method that utilizes dynamic feedback loops from an oracle. Our findings demonstrate that this closed-loop methodology is both practical and effective for automated feature discovery, which is recognized as one of the most challenging phases in current recommender system development.

Problem

• Automate feature extraction from unstructured text for recommendations
• Identify valuable text aspects for models and AutoML pipelines
• Develop automated prompt-engineering tuning with dynamic feedback

Innovation

• LLM-driven agent system for text feature extraction
• Automated prompt-engineering tuning with feedback loops
• Multi-agent coordination for valuable text aspects