Deep Research for Recommender Systems
This work aims to improve user experience in recommender systems by transforming them from passive filters to active assistants, benefiting users who struggle with item exploration and comparison.
This paper addresses the limitation of traditional recommender systems that present item lists, proposing a "deep research" paradigm where the system acts as an active assistant. They introduce RecPilot, a multi-agent framework that generates comprehensive, user-centric reports instead of item lists, which significantly reduces user effort in item evaluation.
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.