Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach

Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach

Jianxun Lian, Fuzheng Zhang, Xing Xie, Guangzhong Sun

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3805-3811. https://doi.org/10.24963/ijcai.2018/529

Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learning techniques especially in visual computing and natural language understanding, more and more researchers have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning-based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi-layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models.
Keywords:
Machine Learning: Neural Networks
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Information Retrieval