Hybrid Session-based News Recommendation using Recurrent Neural Networks
Machine Learning
2020-06-24 v1 Information Retrieval
Machine Learning
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.
Cite
@article{arxiv.2006.13063,
title = {Hybrid Session-based News Recommendation using Recurrent Neural Networks},
author = {Gabriel de Souza P. Moreira and Dietmar Jannach and Adilson Marques da Cunha},
journal= {arXiv preprint arXiv:2006.13063},
year = {2020}
}
Comments
From the Proceeding of the LatinX in AI Research (LXAI) at ICML 2020. arXiv admin note: text overlap with arXiv:1904.10367