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Found 6 papers, 4 papers with code
Hybrid Session-based News Recommendation using Recurrent Neural Networks
We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation that is able to leverage a variety of information types using Recurrent Neural Networks.
Bottom-Up Meta-Policy Search
Despite of the recent progress in agents that learn through interaction, there are several challenges in terms of sample efficiency and generalization across unseen behaviors during training.
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, a setting named session-based recommendation.
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
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.
Learning Humanoid Robot Motions Through Deep Neural Networks
Controlling a high degrees of freedom humanoid robot is acknowledged as one of the hardest problems in Robotics.
News Session-Based Recommendations using Deep Neural Networks
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.
