Interaction Graph Neural Network for News Recommendation

Yongye Qian, Pengpeng Zhao, Zhixu Li, Junhua Fang, Lei Zhao, Victor S. Sheng, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Personalized news recommendation has become a highly challenging problem in recent years. Traditional ID-based methods such as collaborative filtering are not suitable for news recommendation due to the extremely rapid update of candidate news. Various content-based methods have been proposed for news recommendation and achieved the state-of-the-art performance. Recently, knowledge-aware news recommendation further improves the performance through discover latent knowledge level connections among the news. However, we argue that the above content-based methods do not fully utilize the collaborative information latent in user-item interactions into user and news representation learning process. In this paper, we propose a new news recommendation model, Interaction Graph Neural Network (IGNN), which integrates a user-item interactions graph and a knowledge graph into the news recommendation model. Specifically, IGNN obtains the representation of users and items with two graphs. One is the knowledge graph, and another is the user-item interaction graph. It learns the content-based feature from knowledge-level and semantic-level with convolutional neural networks and fuses the high-order collaborative signals extracted from the user-item interaction graph into user and news representation learning process with a graph neural network. Extensive experiments are conducted on the two real-world news data sets, and experimental results show that IGNN significantly outperforms the state-of-the-art approaches for news recommendation.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings
EditorsReynold Cheng, Nikos Mamoulis, Yizhou Sun, Xin Huang
PublisherSpringer
Pages599-614
Number of pages16
ISBN (Print)9783030342227
DOIs
StatePublished - 2019
Event20th International Conference on Web Information Systems Engineering, WISE 2019 - Hongkong, Hong Kong
Duration: Nov 26 2019Nov 30 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11881 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Web Information Systems Engineering, WISE 2019
Country/TerritoryHong Kong
CityHongkong
Period11/26/1911/30/19

Keywords

  • Graph Neural Network
  • Knowledge graph
  • News recommendation

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