Relation Preference Oriented High-order Sampling for Recommendation.

The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results. Therefore, we propose a Relation Preference oriented High-order Sampling (RPHS) model to dynamically sample subgraphs based on relation preference and hard negative samples for user-item pairs. We design a path sampling strategy based on relation preference, which can encode the critical paths between specific user-item pairs to sample the paths in the high-order message passing subgraphs. Next, we design a mixed sampling strategy and define a new propagation operation to further enhance RPHS's ability to distinguish negative signals. Through the above sampling strategies, our model can better aggregate local relation preference information and reveal the internal relationship between users' preferences. Experiments show that our model outperforms the state-of-the-art models on three datasets by 14.98%, 5.31%, and 8.65%, and also performs well in terms of interpretability. The codes are available at https://github.com/RPHS/RPHS.git