GUIDER: Generative User Interest Discovery via Explicit Reasoning with Large Language Models

Abstract

The emergence of Large Language Models (LLMs) has driven a paradigm shift in sequential recommendation from discriminative ranking to generative modeling. However, existing generative approaches predominantly rely on semantic IDs (SIDs)—discrete identifiers derived from hierarchical quantization that function as semantic black boxes. Solely designed for in-domain reconstruction, these opaque tokens preclude the mining of explicit user behavioral logic. In contrast, plain-text modeling unlocks the full inferential potential of LLMs, enabling superior generalization and precise, logic-driven interest discovery. To bridge this gap, we propose GUIDER (Generative User Interest Discovery & Explicit Reasoning), a framework that fundamentally decouples intent planning from item matching by reformulating sequential modeling within a rigorous closed-set semantic interest space. GUIDER first internalizes domain-specific semantics via continual pre-training and activates explicit reasoning through a hindsight-guided chain-of-thought strategy. Crucially, to ensure precise optimization of the generated interest set, we introduce Token-Adaptive Policy Optimization (TAPO). Addressing the credit assignment problem—where standard reinforcement learning indiscriminately assigns identical advantages to the entire response regardless of local correctness—TAPO employs a token-level advantage adaptation mechanism. This strategy explicitly differentiates valid interest circles from hallucinations by dynamically redistributing advantages based on fine-grained quality, while a dynamic thinking penalty suppresses redundant reasoning. Extensive offline evaluations and large-scale online A/B testing on the Kuaishou platform validate that GUIDER not only achieves superior accuracy but also yields consistent gains in average app duration and effectively facilitates out-of-cocoon interest exploration.