# Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1904.10367 · HTML: https://mlsanity.com/paper/1904.10367/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://mlsanity.com/paper/2205.06058/) (2022)
- [Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation](https://mlsanity.com/paper/2107.05124/) (2021)
- [Implicit Session Contexts for Next-Item Recommendations](https://mlsanity.com/paper/2208.09076/) (2022)
- [Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search](https://mlsanity.com/paper/2107.07173/) (2021)
- [Improving Next-Application Prediction with Deep Personalized-Attention Neural Network](https://mlsanity.com/paper/2111.11296/) (2021)
- [Multi-Objective Recommender Systems: Survey and Challenges](https://mlsanity.com/paper/2210.10309/) (2022)
- [Contextual Bandits with Sparse Data in Web setting](https://mlsanity.com/paper/2105.02873/) (2021)
- [A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System](https://mlsanity.com/paper/2407.09137/) (2024)

## Abstract (arXiv)

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 contextual 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: Artificial Neural Networks, Context-Aware Recommender Systems, Hybrid Recommender Systems, News Recommender Systems, Session-based Recommendation