基于深度学习的新闻推荐算法研究综述

doi:10.3778/j.issn.1673-9418.2007021

摘要/Abstract

摘要：

新闻推荐（NR）可以有效缓解新闻信息过载，是当今人们获取新闻资讯的重要方式，而深度学习（DL）成为近年来促进新闻推荐发展的主流技术，使新闻推荐的效果得到显著提升，受到研究者们的广泛关注。主要对基于深度学习的新闻推荐方法研究现状进行分类梳理和分析归纳。根据对新闻推荐的核心对象——用户和新闻的建模思路不同，将基于深度学习的新闻推荐方法分为“两段式”方法、“融合式”方法和“协同式”方法三类。在每类方法中，根据建模过程中的具体子任务或基于的数据组织结构进行更进一步细分，对代表性模型进行分析介绍，评价其优点和局限性等，并详细总结每类方法的特点和优缺点。另外还介绍了新闻推荐中常用数据集、基线算法和性能评价指标，最后分析展望了该领域未来可能的研究方向及发展趋势。

Abstract:

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.

Key words: news recommendation (NR), deep learning (DL), user interest modeling, news modeling

田萱, 丁琪, 廖子慧, 孙国栋. 基于深度学习的新闻推荐算法研究综述[J]. 计算机科学与探索, 2021, 15(6): 971-998.

TIAN Xuan, DING Qi, LIAO Zihui, SUN Guodong. Survey on Deep Learning Based News Recommendation Algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 971-998.

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