The graph-based recommendation has achieved great success in recent years.
However, most existing graph-based recommendations focus on capturing user
preference based on positive edges/feedback, while ignoring negative
edges/feedback (e.g., dislike, low rating) that widely exist in real-world
recommender systems. How to utilize negative feedback in graph-based
recommendations still remains underexplored. In this study, we first conducted
a comprehensive experimental analysis and found that (1) existing graph neural
networks are not well-suited for modeling negative feedback, which acts as a
high-frequency signal in a user-item graph. (2) The graph-based recommendation
suffers from the representation degeneration problem. Based on the two
observations, we propose a novel model that models positive and negative
feedback from a frequency filter perspective called Dual-frequency Graph Neural
Network for Sign-aware Recommendation (DFGNN). Specifically, in DFGNN, the
designed dual-frequency graph filter (DGF) captures both low-frequency and
high-frequency signals that contain positive and negative feedback.
Furthermore, the proposed signed graph regularization is applied to maintain
the user/item embedding uniform in the embedding space to alleviate the
representation degeneration problem. Additionally, we conduct extensive
experiments on real-world datasets and demonstrate the effectiveness of the
proposed model. Codes of our model will be released upon acceptance.

推荐系统中的负反馈在基于图的推荐中被广泛忽视，本研究提出了一种名为 DFGNN 的模型，通过设计双频图滤波器 (DGF) 来捕捉包含正反馈和负反馈的低频和高频信号，并应用有符号图正则化来解决表示退化问题。

DFGNN：双频图神经网络用于符号感知反馈

DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

Graph collaborative filtering (GCF) is a popular technique for capturing
high-order collaborative signals in recommendation systems. However, GCF's
bipartite adjacency matrix, which defines the neighbors being aggregated based
on user-item interactions, can be noisy for users/items with abundant
interactions and insufficient for users/items with scarce interactions.
Additionally, the adjacency matrix ignores user-user and item-item
correlations, which can limit the scope of beneficial neighbors being
aggregated.
In this work, we propose a new graph adjacency matrix that incorporates
user-user and item-item correlations, as well as a properly designed user-item
interaction matrix that balances the number of interactions across all users.
To achieve this, we pre-train a graph-based recommendation method to obtain
users/items embeddings, and then enhance the user-item interaction matrix via
top-K sampling. We also augment the symmetric user-user and item-item
correlation components to the adjacency matrix. Our experiments demonstrate
that the enhanced user-item interaction matrix with improved neighbors and
lower density leads to significant benefits in graph-based recommendation.
Moreover, we show that the inclusion of user-user and item-item correlations
can improve recommendations for users with both abundant and insufficient
interactions. The code is in https://github.com/zfan20/GraphDA.

本文提出了一种新的图矩阵和用户 - 项目交互矩阵的设计方法，将用户 - 用户、项目 - 项目相关性以及用户 - 项目交互数量平衡考虑在内，提高了高阶协同信号的捕获质量，推荐效果显著提升。

图协同信号去噪与增强用于推荐

Graph Collaborative Signals Denoising and Augmentation for  Recommendation

This paper studies graph-based recommendation, where an interaction graph is
constructed from historical records and is lever-aged to alleviate data
sparsity and cold start problems. We reveal an early summarization problem in
existing graph-based models, and propose Neighborhood Interaction (NI) model to
capture each neighbor pair (between user-side and item-side) distinctively. NI
model is more expressive and can capture more complicated structural patterns
behind user-item interactions. To further enrich node connectivity and utilize
high-order structural information, we incorporate extra knowledge graphs (KGs)
and adopt graph neural networks (GNNs) in NI, called Knowledge-enhanced
NeighborhoodInteraction (KNI). Compared with the state-of-the-art
recommendation methods,e.g., feature-based, meta path-based, and KG-based
models, our KNI achieves superior performance in click-through rate prediction
(1.1%-8.4% absolute AUC improvements) and out-performs by a wide margin in
top-N recommendation on 4 real-world datasets.

本文研究了基于图的推荐系统，提出了一种邻域交互模型（NI）来处理数据稀疏性和冷启动问题，通过使用知识图谱和图神经网络（GNN），提出了知识增强的邻域交互（KNI）模型，实现了优越的性能表现。