Iterative Semantic Reasoning from Individual to Group Interests for Generative Recommendation with LLMs

2026-03-14

AI Summary

This work addresses the challenge that traditional recommender systems struggle to effectively model the interplay between explicit individual interests and implicit group interests, thereby limiting a comprehensive understanding of user preferences. To bridge this gap, the paper proposes an Iterative Semantic Reasoning Framework (ISRF), which introduces, for the first time in generative recommendation, a bidirectional iterative reasoning mechanism between individual and group interests. ISRF leverages large language models to perform multi-step attribute reasoning, constructs semantic interaction graphs, models similar-user graphs, and employs an iterative batch optimization strategy to dynamically integrate and mutually enhance both types of interests. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that the proposed method significantly outperforms current state-of-the-art approaches, confirming its effectiveness and strong generalization capability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Recommendation systems aim to learn user interests from historical behaviors and deliver relevant items. Recent methods leverage large language models (LLMs) to construct and integrate semantic representations of users and items for capturing user interests. However, user behavior theories suggest that truly understanding user interests requires not only semantic integration but also semantic reasoning from explicit individual interests to implicit group interests. To this end, we propose an Iterative Semantic Reasoning Framework (ISRF) for generative recommendation. ISRF leverages LLMs to bridge explicit individual interests and implicit group interests in three steps. First, we perform multi-step bidirectional reasoning over item attributes to infer semantic item features and build a semantic interaction graph capturing users' explicit interests. Second, we generate semantic user features based on the semantic item features and construct a similarity-based user graph to infer the implicit interests of similar user groups. Third, we adopt an iterative batch optimization strategy, where individual explicit interests directly guide the refinement of group implicit interests, while group implicit interests indirectly enhance individual modeling. This iterative process ensures consistent and progressive interest reasoning, enabling more accurate and comprehensive user interest learning. Extensive experiments on the Sports, Beauty, and Toys datasets demonstrate that ISRF outperforms state-of-the-art baselines. The code is available at https://github.com/htired/ISRF.

Problem

• generative recommendation
• large language models
• user interests
• semantic reasoning
• group interests

Innovation

• Iterative Semantic Reasoning
• Generative Recommendation
• Large Language Models
• Explicit and Implicit Interests
• Semantic Interaction Graph