Enhancing Recommender Systems Using Textual Embeddings from Pre-trained Language Models
This work addresses the need for more personalized and context-aware recommendations in recommender systems, but it is incremental as it applies existing language models to a known domain.
The paper tackled the problem of traditional recommender systems relying on explicit features by using textual embeddings from pre-trained language models to capture deeper semantic relationships, resulting in significantly improved recommendation accuracy and relevance.
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.