Hybrid Session-based News Recommendation using Recurrent Neural Networks

TL;DR

This paper introduces CHAMELEON, a hybrid RNN-based architecture for session-based news recommendation that effectively incorporates various information types, improving accuracy and coverage in realistic scenarios.

Contribution

The paper presents a novel hybrid meta-architecture called CHAMELEON that combines RNNs with multiple information sources for enhanced news recommendation.

Findings

RNNs improve sequence modeling of session clicks

Leveraging side information boosts recommendation accuracy

Achieves higher catalog coverage than existing algorithms

Abstract

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.