RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.
Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.
The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article.
This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks.