LLM-Based Aspect Augmentations for Recommendation Systems

Abstract

Large language models (LLMs) have shown to be effective in different task settings, including recommendation-related tasks. In this study, we aim at measuring the effectiveness of using item aspects (justifications for users' intentions when buying the item) generated by LLMs in improving the results for ranking tasks. For this purpose, we carefully design prompts for LLMs to derive aspects for items using their textual data in an eCommerce setting. The extracted aspects are used as augmentations for Learning-to-Rank models. Specifically, we input the generated aspects as summarized embeddings using three approaches: (i) augmenting using feature concatenation, (ii) adding a wide aspect component beside a deep component of features, and (iii) adding an aspect embedding tower to create a two-tower model. We conduct extensive experiments on real-world eCommerce dataset and show the effectiveness of including LLM-based aspects in improving ranking metrics such as MRR and NDCG, even when they are compared to models augmented by pre-trained language models (PLM).