Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks

Recommender systems help users deal with information overload by providing tailored item suggestions to them. 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. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. 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 considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty. Keywords: News Recommender Systems, Session-based Recommendation, Artificial Neural Networks, Context-awareness, Hybridization


Hybrid Session-based News Recommendation using Recurrent Neural Networks

We describe a hybrid meta-architecture – the CHAMELEON – for session-bas...

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and ...

Hybrid Model with Time Modeling for Sequential Recommender Systems

Deep learning based methods have been used successfully in recommender s...

CHAMELEON: A Deep Learning Meta-Architecture for News Recommender Systems [Phd. Thesis]

Recommender Systems (RS) have became a popular research topic and, since...

Session-based Recommendations with Recurrent Neural Networks

We apply recurrent neural networks (RNN) on a new domain, namely recomme...

Modeling the Past and Future Contexts for Session-based Recommendation

Long session-based recommender systems have attacted much attention rece...

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems

Recurrent neural networks have proven effective in modeling sequential u...

Please sign up or login with your details

Forgot password? Click here to reset