  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.
