The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF). Following the same GNNs wave, recommender systems exploiting Knowledge Graphs (KGs) have also been successfully empowered by the GCF rationale to combine the representational power of GNNs with the semantics conveyed by KGs, giving rise to Knowledge-aware Graph Collaborative Filtering (KGCF), which use KGs to mine hidden user intent. Nevertheless, empirical evidence suggests that computing and combining user-level intent might not always be necessary, as simpler approaches can yield comparable or superior results while keeping explicit semantic features. Under this perspective, user historical preferences become essential to refine the KG and retain the most discriminating features, thus leading to concise item representation. Driven by the assumptions above, we propose KGUF, a KGCF model that learns latent representations of semantic features in the KG to better define the item profile. By leveraging user profiles through decision trees, KGUF effectively retains only those features relevant to users. Results on three datasets justify KGUF's rationale, as our approach is able to reach performance comparable or superior to SOTA methods while maintaining a simpler formalization. Link to the repository: https://github.com/sisinflab/KGUF.

翻译：图神经网络（GNN）近期被整合到推荐系统中，催生了协同过滤（CF）方法的新分支，即图协同过滤（GCF）。紧随GNN浪潮，利用知识图谱（KG）的推荐系统也借助GCF范式成功增强了GNN的表示能力与KG所传达语义的结合，产生了知识感知图协同过滤（KGCF），其利用KG挖掘用户隐含意图。然而，实验证据表明，计算和组合用户级意图并非总是必要，因为更简单的方法在保持显式语义特征的同时可取得相当或更优的结果。基于这一视角，用户历史偏好成为精炼KG并保留最具区分性特征的关键，从而形成简洁的物品表示。受上述假设驱动，我们提出KGUF——一种KGCF模型，通过学习KG中语义特征的潜在表示以更完善地定义物品画像。通过利用决策树构建用户画像，KGUF能有效仅保留与用户相关的特征。在三个数据集上的实验结果验证了KGUF的合理性：我们的方法在保持更简单形式化的同时，性能可媲美甚至超越当前最优方法。代码仓库：https://github.com/sisinflab/KGUF。