用大模型动态捕捉用户意图，提升推荐精准度。
Enhancing User Intent for Recommendation Systems via Large Language Models
- 结合LSTM与大模型，从行为序列中生成动态意图提示。
- 在ML-1M、Games、Bundle数据集上超越多个基线模型。
- 适合需要实时适应用户变化的推荐场景。
推荐系统在各类在线平台中对提升用户体验和参与度至关重要。传统方法如协同过滤（CF）和基于内容的过滤（CBF）主要依赖历史用户行为或物品特征，难以捕捉用户偏好的动态演变。为此，我们提出DUIP（动态用户意图预测）框架，将LSTM网络与大语言模型（LLMs）结合，以动态捕捉用户意图并生成个性化推荐。LSTM模块建模用户行为的序列与时间依赖性，而大模型则利用LSTM生成的提示预测用户下一个感兴趣物品。在三个不同数据集ML-1M、Games和Bundle上的实验表明，DUIP优于多种基线模型，展现出解决冷启动问题和实时意图适应的能力。基于近期用户交互生成的动态提示，使DUIP能够提供更准确、上下文感知且个性化的推荐。研究结果表明，DUIP是下一代推荐系统的有前景方案，未来可在跨模态推荐和可扩展性方面进一步优化。
原文摘要 · Abstract (English)
Recommendation systems play a critical role in enhancing user experience and engagement in various online platforms. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), rely heavily on past user interactions or item features. However, these models often fail to capture the dynamic and evolving nature of user preferences. To address these limitations, we propose DUIP (Dynamic User Intent Prediction), a novel framework that combines LSTM networks with Large Language Models (LLMs) to dynamically capture user intent and generate personalized item recommendations. The LSTM component models the sequential and temporal dependencies of user behavior, while the LLM utilizes the LSTM-generated prompts to predict the next item of interest. Experimental results on three diverse datasets ML-1M, Games, and Bundle show that DUIP outperforms a wide range of baseline models, demonstrating its ability to handle the cold-start problem and real-time intent adaptation. The integration of dynamic prompts based on recent user interactions allows DUIP to provide more accurate, context-aware, and personalized recommendations. Our findings suggest that DUIP is a promising approach for next-generation recommendation systems, with potential for further improvements in cross-modal recommendations and scalability.
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