# Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models

arXiv: https://arxiv.org/abs/2312.16275 · HTML: https://mlsanity.com/paper/2312.16275/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2023.

## Built on by (most cited first, 13 of 13)

- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [Efficient Inference for Large Language Model-based Generative Recommendation](https://mlsanity.com/paper/2410.05165/) (2024)
- [Graph Foundation Models for Recommendation: A Comprehensive Survey](https://mlsanity.com/paper/2502.08346/) (2025)
- [TINA: Text-Free Inversion Attack for Unlearned Text-to-Image Diffusion Models](https://mlsanity.com/paper/2603.17828/) (2026)
- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation](https://mlsanity.com/paper/2505.10940/) (2025)
- [HADSF: Aspect Aware Semantic Control for Explainable Recommendation](https://mlsanity.com/paper/2510.26994/) (2025)
- [Bridging Semantic Understanding and Popularity Bias with LLMs](https://mlsanity.com/paper/2601.09478/) (2026)
- [TINA+: Probing Residual Visual Knowledge in Unlearned Diffusion Models via Diffusion-Consistent Text-Free Inversion](https://mlsanity.com/paper/2608.17747/) (2026)
- [End-to-End Personalization: Unifying Recommender Systems with Large Language Models](https://mlsanity.com/paper/2508.01514/) (2025)
- [Breaking the Information Silo: Semantic Personas for Cross-Domain Recommendation](https://mlsanity.com/paper/2606.01783/) (2026)
- [Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation](https://mlsanity.com/paper/2606.20554/) (2026)

## Abstract (arXiv)

Recommendation systems harness user-item interactions like clicks and reviews to learn their representations. Previous studies improve recommendation accuracy and interpretability by modeling user preferences across various aspects and intents. However, the aspects and intents are inferred directly from user reviews or behavior patterns, suffering from the data noise and the data sparsity problem. Furthermore, it is difficult to understand the reasons behind recommendations due to the challenges of interpreting implicit aspects and intents. Inspired by the deep semantic understanding offered by large language models (LLMs), we introduce a chain-based prompting approach to uncover semantic aspect-aware interactions, which provide clearer insights into user behaviors at a fine-grained semantic level. To incorporate the abundant interactions of various aspects, we propose the simple yet effective Semantic Aspect-based Graph Convolution Network (short for SAGCN). By performing graph convolutions on multiple semantic aspect graphs, SAGCN efficiently combines embeddings across multiple semantic aspects for final user and item representations. The effectiveness of the SAGCN was evaluated on three publicly available datasets through extensive experiments, which revealed that it outperforms all other competitors. Furthermore, interpretability analysis experiments were conducted to demonstrate the interpretability of incorporating semantic aspects into the model.