  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.
