M-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs

2025-08-21

AI Summary

Existing recommender systems struggle with cold-start and data sparsity, relying either on noisy pseudo-interactions or static semantic assumptions that ignore the dynamic evolution of user motivations. Method: This paper proposes a motivation-aware, large language model (LLM)-driven recommendation framework. It introduces three novel components: a Motivation-Oriented Profile Extractor (MOPE), a Motivation Trait Encoder (MOTE), and a Motivation-Aligned Recommender (MAR), enabling end-to-end implicit motivation disentanglement from sparse interactions, dynamic user profiling, and cross-domain motivation alignment—without generating redundant pseudo-sequences or presupposing semantic similarity. Contribution/Results: By leveraging LLMs to explicitly model time-varying motivational signals underlying user behavior, our approach avoids heuristic assumptions and achieves state-of-the-art performance across multiple benchmark datasets, with particularly significant gains in cold-start settings—demonstrating improved personalization accuracy and generalization robustness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Recommender Systems
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• 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

Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant or noisy signals, or rely heavily on semantic similarity, overlooking dynamic shifts in user motivation. To address these limitations, this paper proposes a novel recommendation framework, termed M-$LLM^3$REC, which leverages large language models for deep motivational signal extraction from limited user interactions. M-$LLM^3$REC comprises three integrated modules: the Motivation-Oriented Profile Extractor (MOPE), Motivation-Oriented Trait Encoder (MOTE), and Motivational Alignment Recommender (MAR). By emphasizing motivation-driven semantic modeling, M-$LLM^3$REC demonstrates robust, personalized, and generalizable recommendations, particularly boosting performance in cold-start situations in comparison with the state-of-the-art frameworks.

Problem

• Addresses cold-start and sparse-data challenges in recommendations
• Extracts motivational signals from limited user interactions
• Reduces noise and captures dynamic user motivation shifts

Innovation

• Leverages LLMs for motivational signal extraction
• Integrates three motivation-oriented modules
• Enhances cold-start recommendation accuracy