Deep Research for Recommender Systems

2026-03-08

AI Summary

This work proposes RecPilot, a novel multi-agent paradigm for in-depth recommendation that moves beyond traditional item-list outputs by automatically generating user-centric, comprehensive natural language reports. Conventional recommender systems place the full burden of exploration, comparison, and decision-making on users, limiting user experience. RecPilot addresses this limitation through the collaboration of two specialized agents: a user-trajectory simulation agent and a self-evolving report generation agent. This approach redefines recommender systems from passive filtering tools into proactive intelligent services. Evaluated on public datasets, RecPilot demonstrates superior user behavior modeling, significantly reduces users’ cognitive load in evaluating recommendations, and produces highly persuasive and decision-supportive reports.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Modeling other Agents
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The technical foundations of recommender systems have progressed from collaborative filtering to complex neural models and, more recently, large language models. Despite these technological advances, deployed systems often underserve their users by simply presenting a list of items, leaving the burden of exploration, comparison, and synthesis entirely on the user. This paper argues that this traditional"tool-based"paradigm fundamentally limits user experience, as the system acts as a passive filter rather than an active assistant. To address this limitation, we propose a novel deep research paradigm for recommendation, which replaces conventional item lists with comprehensive, user-centric reports. We instantiate this paradigm through RecPilot, a multi-agent framework comprising two core components: a user trajectory simulation agent that autonomously explores the item space, and a self-evolving report generation agent that synthesizes the findings into a coherent, interpretable report tailored to support user decisions. This approach reframes recommendation as a proactive, agent-driven service. Extensive experiments on public datasets demonstrate that RecPilot not only achieves strong performance in modeling user behaviors but also generates highly persuasive reports that substantially reduce user effort in item evaluation, validating the potential of this new interaction paradigm.

Problem

• recommender systems
• user experience
• item exploration
• decision support
• recommendation paradigm

Innovation

• deep research
• multi-agent recommendation
• user trajectory simulation
• self-evolving report generation
• proactive recommender system