• Corpus ID: 243860938

Neural News Recommendation with Event Extraction

  title={Neural News Recommendation with Event Extraction},
  author={Songqiao Han and Hailiang Huang and Jiangwei Liu},
A key challenge of online news recommendation is to help users find articles they are interested in. Traditional news recommendation methods usually use single news information, which is insufficient to encode news and user representation. Recent research uses multiple channel news information, e.g., title, category, and body, to enhance news and user representation. However, these methods only use various attention mechanisms to fuse multi-view embeddings without considering deep digging… 

Figures and Tables from this paper

Syntactic-GCN Bert based Chinese Event Extraction
This paper proposes an integrated framework to perform Chinese event extraction that integrates semantic features and syntactic features and evaluates the model on a real-world dataset.
Personalized News Recommendation: Methods and Challenges
A novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges is proposed and proposed instead of following the conventional taxonomy of news recommendation methods.


Neural News Recommendation with Attentive Multi-View Learning
A neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information and can effectively improve the performance of news recommendation is proposed.
DKN: Deep Knowledge-Aware Network for News Recommendation
A deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation and achieves substantial gains over state-of-the-art deep recommendation models is proposed.
DAN: Deep Attention Neural Network for News Recommendation
The proposed DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention- based RNN for capturing richer hidden sequential features of user's clicks, and combines these features for new recommendation.
Graph Enhanced Representation Learning for News Recommendation
A news recommendation method which can enhance the representation learning of users and news by modeling their relatedness in a graph setting and improved performances on a large-scale real-world dataset validate the effectiveness of this proposed method.
Giveme5W1H: A Universal System for Extracting Main Events from News Articles
An in-depth description of an improved version of Giveme5W1H, a system that uses syntactic and domain-specific rules to automatically extract the relevant phrases from English news articles to provide answers to 5W 1H questions, which alone can sufficiently summarize the main event reported on in a news article.
  • Bo Ye
  • Computer Science
  • 2019
Overall, this study makes contributions to information systems (IS) and electronic commerce literature and practice and suggests that a hybrid solution as presented by the proposed system could better serve readers of academic journal to enhance service quality and user satisfaction.
Semantic-Enhanced and Context-Aware Hybrid Collaborative Filtering for Event Recommendation in Event-Based Social Networks
A semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation, which combines semantic content analysis and contextual event influence for user neighborhood selection is proposed.
A Survey of event extraction methods from text for decision support systems
Neural Graph Collaborative Filtering
This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.