Large Language Models (LLMs) have revolutionized text comprehension, leading
to State-of-the-Art (SOTA) news recommendation models that utilize LLMs for
in-depth news understanding. Despite this, accurately modeling user preferences
remains challenging due to the inherent uncertainty of click behaviors.
Techniques like multi-head attention in Transformers seek to alleviate this by
capturing interactions among clicks, yet they fall short in integrating
explicit feedback signals. User Dwell Time emerges as a powerful indicator,
offering the potential to enhance the weak signals emanating from clicks.
Nonetheless, its real-world applicability is questionable, especially when
dwell time data collection is subject to delays. To bridge this gap, this paper
proposes two novel and robust dwell time injection strategies, namely Dwell
time Weight (DweW) and Dwell time Aware (DweA). Dwe} concentrates on refining
Effective User Clicks through detailed analysis of dwell time, integrating with
initial behavioral inputs to construct a more robust user preference. DweA
empowers the model with awareness of dwell time information, thereby
facilitating autonomous adjustment of attention values in user modeling. This
enhancement sharpens the model's ability to accurately identify user
preferences. In our experiment using the real-world news dataset from MSN
website, we validated that our two strategies significantly improve
recommendation performance, favoring high-quality news. Crucially, our
approaches exhibit robustness to user dwell time information, maintaining their
ability to recommend high-quality content even in extreme cases where dwell
time data is entirely missing.

该论文提出了两种新的可靠的停留时间注入策略，即停留时间权重（DweW）和停留时间感知（DweA），通过对停留时间的详细分析，改进了有效用户点击，并结合初始行为输入构建了更可靠的用户偏好，从而增强了模型准确识别用户偏好的能力。在使用 MSN 网站的真实新闻数据集的实验中，验证了我们的两种策略显著提高了推荐性能，偏好高质量新闻。关键是，我们的方法对用户停留时间信息具有健壮性，即使在停留时间数据完全丢失的极端情况下，仍能推荐高质量内容。