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.
翻译:对于许多新闻门户来说,为匿名读者提供新闻建议是一项有用但具有挑战性的任务,在这些门户中,读者与文章之间的互动在临时登录会中受到限制。以前的著作倾向于将会议为基础的建议拟订为下一个项目预测任务,而忽视用户行为的暗含反馈,这表明用户真正喜欢或不喜欢什么。因此,我们提出了一个综合框架,通过积极的反馈(即他们花更多的时间讨论的文章)和消极反馈(即他们选择不点击而跳过的文章)来模拟用户的行为。 此外,框架暗含了用户使用届会开始时间的模型,以及使用最初出版时间(我们称之为“中性反馈”)的文章,我们称之为“中性反馈”,对三个真实世界新闻数据集的实证评估显示,与其它基于会议的最新建议方法相比,框架在更准确、多样、甚至出人意料之外的建议方面表现良好。