  Graph Convolution Networks (GCNs) have significantly succeeded in learning
user and item representations for recommendation systems. The core of their
efficacy is the ability to explicitly exploit the collaborative signals from
both the first- and high-order neighboring nodes. However, most existing
GCN-based methods overlook the multiple interests of users while performing
high-order graph convolution. Thus, the noisy information from unreliable
neighbor nodes (e.g., users with dissimilar interests) negatively impacts the
representation learning of the target node. Additionally, conducting graph
convolution operations without differentiating high-order neighbors suffers the
over-smoothing issue when stacking more layers, resulting in performance
degradation. In this paper, we aim to capture more valuable information from
high-order neighboring nodes while avoiding noise for better representation
learning of the target node. To achieve this goal, we propose a novel GCN-based
recommendation model, termed Cluster-based Graph Collaborative Filtering
(ClusterGCF). This model performs high-order graph convolution on
cluster-specific graphs, which are constructed by capturing the multiple
interests of users and identifying the common interests among them.
Specifically, we design an unsupervised and optimizable soft node clustering
approach to classify user and item nodes into multiple clusters. Based on the
soft node clustering results and the topology of the user-item interaction
graph, we assign the nodes with probabilities for different clusters to
construct the cluster-specific graphs. To evaluate the effectiveness of
ClusterGCF, we conducted extensive experiments on four publicly available
datasets. Experimental results demonstrate that our model can significantly
improve recommendation performance.
