| Summary: | News recommendation (NR) can effectively alleviate the overload of news information, and it is an important way to obtain news information for users. Deep learning (DL) has become a mainstream technology to promote the development of NR in recent years, and the effect of news recommendation has been significantly improved, which is widely concerned by researchers. In this paper, the methods of deep learning-based news recommendation (DNR) are classified, analyzed and summarized. In the research of NR, modeling users or news are two key tasks. According to different strategies of modeling users or news, the news recommendation methods based on deep learning are divided into three types: “two-stage” method, “fusion” method and “collaboration” method. Each type of method is further subdivided in terms of sub-tasks or the data organization structure based on. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The characteristics, advantages and disadvantages of each type of methods are also summarized in detail. Furthermore, the commonly used datasets, baseline and performance evaluation indicators are introduced. Finally, the possible future research directions and development trends in this field are analyzed and predicted.
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