论文标题
积极，消极和中立：基于会话的新闻建议中的隐性反馈建模
Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation
论文作者
论文摘要
匿名读者的新闻建议是许多新闻门户的有用但具有挑战性的任务，在临时登录会议中，读者和文章之间的交互受到限制。先前的工作倾向于将基于会话的建议作为下一个项目预测任务，而他们忽略了用户行为的隐式反馈，这表明了用户的真正喜欢或不喜欢的内容。因此，我们提出了一个综合框架，以通过积极的反馈（即，他们花费更多的时间上的文章）和负面反馈（即，他们选择跳过而无需单击的文章）对用户行为进行建模。此外，该框架使用其会话开始时间暗中对用户进行建模，并使用其初始发布时间（我们称为“中性反馈”）对用户进行建模。在三个现实世界新闻数据集上进行的经验评估表明，该框架比其他基于最新的会话推荐方法更准确，多样化甚至意外建议的表现。
News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). Moreover, the framework implicitly models the user using their session start time, and the article using its initial publishing time, in what we call "neutral feedback". Empirical evaluation on three real-world news datasets shows the framework's promising performance of more accurate, diverse and even unexpectedness recommendations than other state-of-the-art session-based recommendation approaches.
