Hybrid Session-based News Recommendation using Recurrent Neural Networks
This work addresses the problem of improving personalized news recommendations for users in online portals, though it appears incremental as it builds on existing session-based methods with a hybrid approach.
The paper tackled session-based news recommendation by proposing a hybrid meta-architecture called CHAMELEON that uses Recurrent Neural Networks to model click sequences and leverage side information, resulting in significantly higher recommendation accuracy and catalog coverage compared to other algorithms.
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. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of modeling the sequence of session clicks with RNNs and leveraging side information about users and articles, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms.