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An effective news recommendation system should harness the historical information of the user based on her interactions as well as the content of the articles. In this paper we propose a novel deep learning model for news recommendation which utilizes the content of the news articles as well as the sequence in which the articles were read by the user. To model both of these information, which are essentially of different types, we propose a simple yet effective architecture which utilizes a 3-dimensional Convolutional Neural Network which takes the word embeddings of the articles present in the user history as its input. Using such a method endows the model with the capability to automatically learn spatial (features of a particular article) as well as temporal features (features across articles read by a user) which signify the interest of the user. At test time, we use this in combination with a 2-dimensional Convolutional Neural Network for recommending articles to users. On a real-world dataset our method outperformed strong baselines which also model the news recommendation problem using neural networks.
Khattar et al. (2018) studied this question.