Abstract

News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.

中文翻译：

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知识感知新闻推荐系统的调查

摘要

随着时间的推移，新闻消费已经从传统媒体转向在线平台，在线平台使用推荐算法，通过根据用户偏好和阅读行为推荐相关文章来帮助用户浏览大量传入的每日新闻。与电影或电子商务等领域相比，推荐系统已被证明非常成功，新闻领域的特征（例如，文章出现和过时的频率高，用户兴趣的动态更大，文章之间的关系不太明确，和缺乏明确的用户反馈）给推荐模型带来了额外的挑战。虽然其中一些可以通过传统的推荐技术来克服，已经提出将外部知识注入新闻推荐系统，以通过捕获文章文本和元数据中不包含的信息和模式来增强推荐，从而解决传统模型的缺点。该调查对知识感知新闻推荐系统进行了全面审查。我们提出了一种分类法，将模型分为三类：神经方法、非神经实体中心方法和非神经基于路径的方法。此外，分析了基础推荐算法及其评估。最后，确定了知识感知新闻推荐领域的开放问题，并提出了潜在的研究方向。解决传统模式的不足。该调查对知识感知新闻推荐系统进行了全面审查。我们提出了一种分类法，将模型分为三类：神经方法、非神经实体中心方法和非神经基于路径的方法。此外，分析了基础推荐算法及其评估。最后，确定了知识感知新闻推荐领域的开放问题，并提出了潜在的研究方向。解决传统模式的不足。该调查对知识感知新闻推荐系统进行了全面审查。我们提出了一种分类法，将模型分为三类：神经方法、非神经实体中心方法和非神经基于路径的方法。此外，分析了基础推荐算法及其评估。最后，确定了知识感知新闻推荐领域的开放问题，并提出了潜在的研究方向。以及他们的评价进行分析。最后，确定了知识感知新闻推荐领域的开放问题，并提出了潜在的研究方向。以及他们的评价进行分析。最后，确定了知识感知新闻推荐领域的开放问题，并提出了潜在的研究方向。