  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 higher-level information contained
in the context. These methods encode news content on the word level and jointly
train the attention parameters in the recommendation network, leading to more
corpora being required to train the model. We propose an Event Extraction-based
News Recommendation (EENR) framework to overcome these shortcomings, utilizing
event extraction to abstract higher-level information. EENR also uses a
two-stage strategy to reduce parameters in subsequent parts of the
recommendation network. We train the Event Extraction module by external
corpora in the first stage and apply the trained model to the news
recommendation dataset to predict event-level information, including event
types, roles, and arguments, in the second stage. Then we fuse multiple channel
information, including event information, news title, and category, to encode
news and users. Extensive experiments on a real-world dataset show that our
EENR method can effectively improve the performance of news recommendations.
Finally, we also explore the reasonability of utilizing higher abstract level
information to substitute news body content.
