用大模型挖掘用户动机，提升冷启动推荐准确率
M-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
- 通过大模型从少量交互中提取用户动机信号
- 冷启动场景下性能优于当前最先进方法
- 适合研究推荐系统动机建模的学者与工程师
推荐系统对改善用户体验和平台效率至关重要，能缓解信息过载并辅助决策。传统方法如基于内容、协同过滤和深度学习虽取得显著成果，但在冷启动和数据稀疏场景仍面临挑战。现有方案或生成伪交互序列引入冗余噪声，或过度依赖语义相似性，忽视用户动机的动态变化。为此，本文提出新框架M-$LLM^3$REC，利用大语言模型从有限用户交互中深度提取动机信号。该框架包含三个模块：动机导向的用户画像提取器（MOPE）、动机导向的特征编码器（MOTE）和动机对齐推荐器（MAR）。通过强调动机驱动的语义建模，M-$LLM^3$REC在冷启动情境下表现出更强的个性化、鲁棒性和泛化能力，显著优于当前最先进框架。
原文摘要 · Abstract (English)
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.
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