用预训练语言模型文本嵌入提升推荐系统精准度
Enhancing Recommender Systems Using Textual Embeddings from Pre-trained Language Models
- 将用户、物品和上下文转为自然语言表示，生成高维语义嵌入
- 实验显示推荐准确率与相关性显著提升，更个性化且考虑上下文
- 适合关注文本理解与推荐融合的研究者或工业应用
近年来，BERT、RoBERTa等预训练语言模型（PLMs）在自然语言处理领域取得突破，推动了对人类语言的深度理解。本文探索利用预训练语言模型生成的文本嵌入来增强推荐系统，以克服传统推荐系统仅依赖用户、物品及交互的显式特征所带来的局限。通过将结构化数据转化为自然语言表示，我们生成了能够捕捉用户、物品与上下文之间深层语义关系的高维嵌入。实验结果表明，该方法显著提升了推荐的准确性和相关性，实现了更具个性化和上下文感知的推荐效果。研究证实了预训练语言模型在提升推荐系统效能方面的巨大潜力。
原文摘要 · Abstract (English)
Recent advancements in language models and pre-trained language models like BERT and RoBERTa have revolutionized natural language processing, enabling a deeper understanding of human-like language. In this paper, we explore enhancing recommender systems using textual embeddings from pre-trained language models to address the limitations of traditional recommender systems that rely solely on explicit features from users, items, and user-item interactions. By transforming structured data into natural language representations, we generate high-dimensional embeddings that capture deeper semantic relationships between users, items, and contexts. Our experiments demonstrate that this approach significantly improves recommendation accuracy and relevance, resulting in more personalized and context-aware recommendations. The findings underscore the potential of PLMs to enhance the effectiveness of recommender systems.
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