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DKN: Deep Knowledge-Aware Network for News Recommendation

Authors:

Minyi GuoAuthors Info & Claims

WWW '18: Proceedings of the 2018 World Wide Web Conference

Pages 1835 - 1844

https://doi.org/10.1145/3178876.3186175

Published: 10 April 2018 Publication History

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Abstract

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.

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Index Terms

DKN: Deep Knowledge-Aware Network for News Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Other conferences

WWW '18: Proceedings of the 2018 World Wide Web Conference

April 2018

2000 pages

ISBN:9781450356398

General Chairs:
Pierre-Antoine Champin
Universitè Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

Copyright © 2018 ACM.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 10 April 2018

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Ministry of Science and Technology of the People's Republic of China

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WWW '18

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WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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WWW '18 Paper Acceptance Rate 170 of 1,155 submissions, 15%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Zhu YWang YLi YQiang JYuan Y(2025)Soft Prompt-tuning with Self-Resource Verbalizer for short text streamsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109589139(109589)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109589
Zhang XGuo J(2024)A feature-enhanced knowledge graph neural network for machine learning method recommendationPeerJ Computer Science10.7717/peerj-cs.228410(e2284)Online publication date: 28-Aug-2024
https://doi.org/10.7717/peerj-cs.2284
Chen Y(2024)A News Recommendation Method for User Privacy ProtectionInternational Journal of Computer Science and Information Technology10.62051/ijcsit.v2n3.042:3(25-36)Online publication date: 28-May-2024
https://doi.org/10.62051/ijcsit.v2n3.04
Jiang SLu YSong HLu ZZhang Y(2024)A Hybrid News Recommendation Approach Based on Title–Content MatchingMathematics10.3390/math1213212512:13(2125)Online publication date: 6-Jul-2024
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