The success of graph neural network-based models (GNNs) has significantly
advanced recommender systems by effectively modeling users and items as a
bipartite, undirected graph. However, many original graph-based works often
adopt results from baseline papers without verifying their validity for the
specific configuration under analysis. Our work addresses this issue by
focusing on the replicability of results. We present a code that successfully
replicates results from six popular and recent graph recommendation models
(NGCF, DGCF, LightGCN, SGL, UltraGCN, and GFCF) on three common benchmark
datasets (Gowalla, Yelp 2018, and Amazon Book). Additionally, we compare these
graph models with traditional collaborative filtering models that historically
performed well in offline evaluations. Furthermore, we extend our study to two
new datasets (Allrecipes and BookCrossing) that lack established setups in
existing literature. As the performance on these datasets differs from the
previous benchmarks, we analyze the impact of specific dataset characteristics
on recommendation accuracy. By investigating the information flow from users'
neighborhoods, we aim to identify which models are influenced by intrinsic
features in the dataset structure. The code to reproduce our experiments is
available at: this https URL

我们的研究聚焦于图神经网络模型的可复制性，通过复现六种热门的图推荐模型在三个常见基准数据集上的结果来解决原有图模型研究中常采用未经验证结果的问题。此外，我们还将这些图模型与传统的协同过滤模型进行比较，并扩展研究至两个缺乏已建立的设置的新数据集，分析特定数据集特征对推荐准确度的影响，通过研究用户邻域信息流动，旨在识别数据集结构中的内在特征对模型的影响。

挑战图形协同过滤的神话：理性和可重复性分析

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and  Reproducibility-driven Analysis

This paper proposes a new combinatorial auction framework for local energy
flexibility markets, which addresses the issue of prosumers' inability to
bundle multiple flexibility time intervals. To solve the underlying NP-complete
winner determination problems, we present a simple yet powerful heterogeneous
tri-partite graph representation and design graph neural network-based models.
Our models achieve an average optimal value deviation of less than 5\% from an
off-the-shelf optimization tool and show linear inference time complexity
compared to the exponential complexity of the commercial solver. Contributions
and results demonstrate the potential of using machine learning to efficiently
allocate energy flexibility resources in local markets and solving optimization
problems in general.

本文提出了一个新的组合拍卖框架，解决了生产和消费者无法绑定多个灵活时间间隔的问题，并通过设计基于图神经网络的模型，解决了基于异构三部图表示的 NP 完全获胜者决策问题。