KGAT: Knowledge graph attention network for recommendation

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...

Full description

Saved in:
Bibliographic Details
Main Authors: WANG, Xiang, HE, Xiangnan, CAO, Yixin, LIU, Meng, CHUA, Tat-Seng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/7287
https://ink.library.smu.edu.sg/context/sis_research/article/8290/viewcontent/3292500.3330989.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-8290
record_format dspace
spelling sg-smu-ink.sis_research-82902022-09-29T07:44:20Z KGAT: Knowledge graph attention network for recommendation WANG, Xiang HE, Xiangnan CAO, Yixin LIU, Meng CHUA, Tat-Seng 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. 2019-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7287 info:doi/10.1145/3292500.3330989 https://ink.library.smu.edu.sg/context/sis_research/article/8290/viewcontent/3292500.3330989.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Collaborative Filtering Recommendation Graph Neural Network Higher-order Connectivity Embedding Propagation Knowledge Graph Databases and Information Systems Graphics and Human Computer Interfaces OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Collaborative Filtering
Recommendation
Graph Neural Network
Higher-order Connectivity
Embedding Propagation
Knowledge Graph
Databases and Information Systems
Graphics and Human Computer Interfaces
OS and Networks
spellingShingle Collaborative Filtering
Recommendation
Graph Neural Network
Higher-order Connectivity
Embedding Propagation
Knowledge Graph
Databases and Information Systems
Graphics and Human Computer Interfaces
OS and Networks
WANG, Xiang
HE, Xiangnan
CAO, Yixin
LIU, Meng
CHUA, Tat-Seng
KGAT: Knowledge graph attention network for recommendation
description 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.
format text
author WANG, Xiang
HE, Xiangnan
CAO, Yixin
LIU, Meng
CHUA, Tat-Seng
author_facet WANG, Xiang
HE, Xiangnan
CAO, Yixin
LIU, Meng
CHUA, Tat-Seng
author_sort WANG, Xiang
title KGAT: Knowledge graph attention network for recommendation
title_short KGAT: Knowledge graph attention network for recommendation
title_full KGAT: Knowledge graph attention network for recommendation
title_fullStr KGAT: Knowledge graph attention network for recommendation
title_full_unstemmed KGAT: Knowledge graph attention network for recommendation
title_sort kgat: knowledge graph attention network for recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/7287
https://ink.library.smu.edu.sg/context/sis_research/article/8290/viewcontent/3292500.3330989.pdf
_version_ 1770576304572203008