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arXiv · 1908.04032

An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation

Yanru Qu·Ting Bai·Weinan Zhang·Jianyun Nie·Jian Tang
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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.

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Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang. 2019-08-12. An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation. https://arxiv.org/abs/1908.04032

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