An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
This addresses data sparsity and cold start problems in recommendation systems, offering a novel approach for improved accuracy, though it appears incremental as it builds on existing graph-based and KG-based methods.
The paper tackles the early summarization problem in graph-based recommendation by proposing a Neighborhood Interaction (NI) model that captures neighbor pairs distinctively, and enhances it with knowledge graphs and GNNs as KNI, achieving 1.1%-8.4% absolute AUC improvements in click-through rate prediction and outperforming state-of-the-art methods in top-N recommendation on four datasets.
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.