Harnessing GNN-LLM Synergy for Predicting and Explaining Restaurant Survival
Authors: Hao Liao, Yong Zhang, Wei Zhang, Hao Cheng, Mingyang Zhou, KeZhong Lu, Xiaoqun Wu, Rui Mao


Reviewer paper summaries: The paper presents a novel approach for predicting restaurant survival and generating explanations using a combination of GNNs and LLMs. The method leverages LLMs to summarize review information and integrates this with interaction data from a bipartite graph using graph contrastive learning. Extensive experiments on Yelp and Dianping datasets demonstrate the superior performance of the proposed model in both prediction and explanation tasks.
Summary of strengths: - The method effectively combines textual data with interaction data, providing a richer representation for predictions.
- The use of fine-tuned LLMs for generating explanations adds a valuable layer of interpretability to the model's predictions.
Summary of weaknesses: - The core contribution is an incremental improvement over existing methods by combining known techniques (GNNs and LLMs) rather than introducing fundamentally new concepts. This limits the novelty of the work.
- What is the computational cost of fine-tuning LLMs in terms of time and resources compared to traditional models?
- The model primarily uses review text and interaction data, potentially overlooking other critical factors such as financial metrics, operational data, and external economic indicators that could influence restaurant survival. This narrow feature set may limit the model's predictive power.
- Are there any specific preprocessing steps required to handle the review data effectively for this model?
Comments, suggestions and typos: See above.
Overall assessment scores: 2.5