Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks down the independent interaction assumption by linking items with their attributes. We argue that in such a hybrid structure of KG and user-item graph, high-order relations — which connect two items with one or multiple linked attributes — are an essential factor for successful recommendation. We propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion. It recursively propagates the embeddings from a node’s neighbors (which can be users, items, or attributes) to refine the node’s embedding, and employs an attention mechanism to discriminate the importance of the neighbors. Our KGAT is conceptually advantageous to existing KG-based recommendation methods, which either exploit highorder relations by extracting paths or implicitly modeling them with regularization. Empirical results on three public benchmarks show that KGAT significantly outperforms state-of-the-art methods like Neural FM [11] and RippleNet [29]. Further studies verify the efficacy of embedding propagation for high-order relation modeling and the interpretability benefits brought by the attention mechanism. We release the codes and datasets at https://github. com/xiangwang1223/knowledge_graph_attention_network.

Keywords

Collaborative Filtering, Recommendation, Graph Neural Network, Higher-order Connectivity, Embedding Propagation, Knowledge Graph

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, 2019 August 4-8

First Page

950

Last Page

958

ISBN

9781450362016

Identifier

10.1145/3292500.3330989

Publisher

ACM

City or Country

Anchorage, USA

Additional URL

http://doi.org/10.1145/3292500.3330989

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