Intent-Guided Reasoning for Sequential Recommendation

2025-12-15

AI Summary

Existing sequential recommendation models suffer from two key limitations: inference instability (i.e., sensitivity to behavioral noise) and superficiality (i.e., modeling only item-level transitions while neglecting deeper behavioral patterns). To address these, we propose an intent-anchored two-stage reasoning framework. First, a latent intent distiller explicitly captures high-order user intents; second, an intent-aware deliberative reasoner enables stable, deep-level inference. Our method incorporates a dual-attention decoupling architecture, a frozen encoder augmented with learnable intent tokens, multi-view intent consistency regularization, and a noise-robust training strategy. Extensive experiments show an average 7.13% improvement over baselines across three public benchmarks; under 20% behavioral noise, performance degrades by only 10.4%, substantially outperforming state-of-the-art methods. Our core contribution is the first explicit use of high-order user intent as a stable reasoning anchor—uniquely balancing robustness, interpretability, and deep behavioral pattern modeling.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

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

Abstract

Sequential recommendation systems aim to capture users' evolving preferences from their interaction histories. Recent reasoningenhanced methods have shown promise by introducing deliberate, chain-of-thought-like processes with intermediate reasoning steps. However, these methods rely solely on the next target item as supervision, leading to two critical issues: (1) reasoning instability--the process becomes overly sensitive to recent behaviors and spurious interactions like accidental clicks, and (2) surface-level reasoning--the model memorizes item-to-item transitions rather than understanding intrinsic behavior patterns. To address these challenges, we propose IGR-SR, an Intent-Guided Reasoning framework for Sequential Recommendation that anchors the reasoning process to explicitly extracted high-level intents. Our framework comprises three key components: (1) a Latent Intent Distiller (LID) that efficiently extracts multi-faceted intents using a frozen encoder with learnable tokens, (2) an Intent-aware Deliberative Reasoner (IDR) that decouples reasoning into intent deliberation and decision-making via a dual-attention architecture, and (3) an Intent Consistency Regularization (ICR) that ensures robustness by enforcing consistent representations across different intent views. Extensive experiments on three public datasets demonstrate that IGR-SR achieves an average 7.13% improvement over state-of-the-art baselines. Critically, under 20% behavioral noise, IGR-SR degrades only 10.4% compared to 16.2% and 18.6% for competing methods, validating the effectiveness and robustness of intent-guided reasoning.

Problem

• Sequential recommendation systems suffer from reasoning instability due to noise.
• Existing methods exhibit surface-level reasoning by memorizing item transitions.
• Models lack understanding of intrinsic user behavior patterns and intents.

Innovation

• Extracts multi-faceted intents using frozen encoder with learnable tokens
• Decouples reasoning into intent deliberation and decision-making via dual-attention
• Ensures robustness by enforcing consistent representations across intent views