News recommendation has become an essential way to help readers discover interesting stories. While a growing line of research has focused on modeling reading preferences for news recommendation, they neglect the instability of reader consumption behaviors, i.e., consumption behaviors of readers may be influenced by other factors in addition to user interests, which degrades the recommendation effectiveness of existing methods. In this article, we propose a probabilistic generative model, BoRe, where user interests and crowd effects are used to adapt to the instability of reader consumption behaviors, and reading sequences are utilized to adapt user interests evolving over time. Further, the extreme sparsity problem in the domain of news severely hinders accurately modeling user interests and reading sequences, which discounts BoRe’s ability to adapt to the instability. Accordingly, we leverage domain-specific features to model user interests in the situation of extreme sparsity. Meanwhile, we consider groups of users instead of individuals to capture reading sequences. Besides, we study how to reduce the computation to allow online application. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of BoRe on real-world datasets. The experimental results show the superiority of BoRe, compared with the state-of-the-art competing methods.

中文翻译：

---

硼

新闻推荐已成为帮助读者发现有趣故事的重要途径。越来越多的研究集中在对新闻推荐的阅读偏好建模上，却忽略了读者消费行为的不稳定性，即读者的消费行为可能会受到除用户兴趣之外的其他因素的影响，从而降低了现有的推荐效果。方法。在本文中，我们提出了一个概率生成模型 BoRe，其中使用用户兴趣和人群效应来适应读者消费行为的不稳定性，并利用阅读序列来适应用户兴趣随时间的变化。此外，新闻领域的极端稀疏问题严重阻碍了对用户兴趣和阅读序列的准确建模，这削弱了 BoRe 适应不稳定性的能力。因此，我们利用特定领域的特征来模拟极端稀疏情况下的用户兴趣。同时，我们考虑用户组而不是个人来捕获阅读序列。此外，我们研究如何减少计算量以允许在线申请。已经进行了广泛的实验来评估 BoRe 在现实世界数据集上的有效性和效率。实验结果表明，与最先进的竞争方法相比，BoRe 的优越性。我们研究如何减少计算以允许在线申请。已经进行了广泛的实验来评估 BoRe 在现实世界数据集上的有效性和效率。实验结果表明，与最先进的竞争方法相比，BoRe 的优越性。我们研究如何减少计算以允许在线申请。已经进行了广泛的实验来评估 BoRe 在现实世界数据集上的有效性和效率。实验结果表明，与最先进的竞争方法相比，BoRe 的优越性。