Intuition-Guided Latent Reasoning for LLM-Based Recommendation

2026-06-25

AI Summary

This work proposes IntuRec, a novel framework that introduces the cognitive neuroscience concept of intuition-guided reasoning into large language model–based recommender systems. Existing approaches often suffer from suboptimal inference trajectories due to unconstrained initialization, leading to misalignment between latent user representations and target item embeddings. IntuRec addresses this by first generating a Top-K candidate set from user history to serve as an intuitive cue, then transforming it into preference-aligned intuition embeddings via self-attention and cross-attention mechanisms. These embeddings provide a semantically coherent starting point to initialize and guide subsequent implicit reasoning. Extensive experiments on multiple real-world datasets demonstrate that IntuRec significantly outperforms state-of-the-art baselines, validating the effectiveness of intuition-guided reasoning in recommendation.

Abstract

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, motivating their use for preference reasoning in recommender systems. Latent reasoning, which operates in continuous hidden spaces rather than discrete tokens, has recently emerged as a promising paradigm for LLM-based recommendation. However, existing methods often start from unconstrained reasoning points, where hidden representations are misaligned with target item embeddings, leading to suboptimal reasoning trajectories. Inspired by cognitive neuroscience, which suggests that human multi-step reasoning is guided by intuition as a latent prior, we propose \emph{IntuRec}, a two-stage framework that anchors latent reasoning with \emph{recommendation intuition}. In the extraction stage, the LLM-based recommender generates a top-$K$ candidate set based on users' histories as the source of intuition. In the injection stage, the candidate set is transformed into a preference-aligned intuition embedding using self- and cross-attention mechanisms, which initializes the reasoning start point and guides subsequent latent reasoning. By providing a semantically grounded starting point, IntuRec efficiently explores the preference space along more accurate reasoning trajectories. Extensive experiments on multiple real-world datasets demonstrate that IntuRec consistently outperforms state-of-the-art baselines. We release our code at https://github.com/Ten-Mao/IntuRec.

Problem

• Latent Reasoning
• LLM-Based Recommendation
• Reasoning Trajectories
• Preference Alignment
• Recommendation Intuition

Innovation

• latent reasoning
• recommendation intuition
• LLM-based recommendation
• attention mechanisms
• preference alignment