An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
arXiv:1908.04032
Abstract
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.
arXiv comment9 pages, accepted by DLP-KDD'19, best paper
References in corpus (10)
- Semi-Supervised Classification with Graph Convolutional Networks
- Inductive Representation Learning on Large Graphs
- NAIS: Neural Attentive Item Similarity Model for Recommendation
- DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
- FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
- DKN: Deep Knowledge-Aware Network for News Recommendation
- GraphGAN: Graph Representation Learning with Generative Adversarial Nets
- Deep Coevolutionary Network: Embedding User and Item Features for Recommendation
- Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
- KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems