Keunchan Park
ABSTRACT
A fundamental role of news websites is to recommend articles that are interesting to read. The key challenge of news recommendation is to recommend newly published articles. Unlike other domains, outdated items are considered to be irrelevant in the news recommendation task. Another challenge is that the recommendation candidates are not seen in the training phase. In this paper, we introduce deep neural network models to overcome these challenges. we propose a modified session-based Recurrent Neural Network (RNN) model tailored to news recommendation as well as a history-based RNN model that spans the whole user's past histories. Finally, we propose a Convolutional Neural Network (CNN) model to capture user preferences and to personalize recommendation results. Experimental results on real-world news dataset shows that our model outperforms competitive baselines.
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Index Terms
- Deep Neural Networks for News Recommendations
Recommendations
News Session-Based Recommendations using Deep Neural Networks
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