Revisiting Deep Learning Models With Click‐Through Rate Feedback in News Recommender Systems

ABSTRACT The continuous increase in news over the social web makes it difficult for users to search for a specific topic of interest. To overcome this difficulty, News Recommender Systems (NRS) are widely used to help users access the right and relevant content. Due to the diverse nature of NRS, researchers have started applying various deep learning (DL) models for the implementation of NRS. To our knowledge, however, current NRS do not look into the click‐through rate (CTR) with DL models. This manuscript proposes a news recommendation method that considers click‐log features with fine‐tuned DL models such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short‐Term Memory (LSTM), and Gated Recurrent Unit (GRU) for news recommendation. The primary objective of this study is to recommend news to users by constructing a click‐rate matrix from the provided datasets. In this regard, the datasets are preprocessed, and then a user‐item matrix based on click‐through rate is constructed. To demonstrate the applicability of the click, log feature matrix with deep learning models for news recommendation, extensive experiments are conducted using two cross‐validation methods, namely K‐fold and holdout. We used precision, Area Under the Curve (AUC), and Normalized Discounted Cumulative Gain (nDCG) to test the performance of the proposed method. According to the results obtained, CNN performed better compared to other DL models. The evaluation results displayed that incorporating the click log feature matrix and DL models can significantly improve the news recommendation results. The study also suggests that the use of the click log feature matrix with DL models is the most effective and simple method for recommending news articles, outperforming the existing approaches.