Proceedings ArticleDOI
DKN: Deep Knowledge-Aware Network for News Recommendation
Hongwei Wang,Fuzheng Zhang,Xing Xie,Minyi Guo +3 more
- pp 1835-1844
TLDR
Wang et al. as mentioned in this paper proposed a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation, which is a content-based deep recommendation framework for click-through rate prediction.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.read more
Citations
More filters
Journal ArticleDOI
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
TL;DR: A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research.
Proceedings ArticleDOI
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
TL;DR: RippleNet as discussed by the authors proposes an end-to-end framework that naturally incorporates the knowledge graph into recommender systems to stimulate the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in a knowledge graph.
Proceedings ArticleDOI
Knowledge Graph Convolutional Networks for Recommender Systems
TL;DR: This paper proposes Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG.
Proceedings ArticleDOI
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
TL;DR: This paper considers knowledge graphs as the source of side information and proposes MKR, a Multi-task feature learning approach for Knowledge graph enhanced Recommendation, a deep end-to-end framework that utilizes knowledge graph embedding task to assist recommendation task.
Journal ArticleDOI
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
TL;DR: A comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research as mentioned in this paper .
References
More filters
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Proceedings Article
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher,Alex Perelygin,Jean Y. Wu,Jason Chuang,Christopher D. Manning,Andrew Y. Ng,Christopher Potts +6 more
TL;DR: A Sentiment Treebank that includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality, and introduces the Recursive Neural Tensor Network.