Abstract
Personalized news recommendation is highly time-sensitive, as user interests are often driven by emerging events, trending topics, and shifting real-world contexts. These dynamics make it essential to model not only users' long-term preferences, which reflect stable reading habits and high-order collaborative patterns, but also their short-term, context-dependent interests that change rapidly over time. However, most existing approaches rely on a single static interaction graph, which struggles to capture both long-term preference patterns and short-term interest changes as user behavior evolves. To address this challenge, we propose a unified framework that learns user preferences from both global and local temporal perspectives. A global preference modeling component captures long-term collaborative signals from the overall interaction graph, while a local preference modeling component partitions historical interactions into stage-wise temporal subgraphs to represent short-term dynamics. Within this module, an LSTM branch models the progressive evolution of recent interests, and a self-attention branch captures long-range temporal dependencies. Extensive experiments on two large-scale real-world datasets show that our approach consistently outperforms strong baselines and delivers fresher and more relevant recommendations across diverse user behaviors and temporal settings.
Abstract (translated)
个性化新闻推荐具有很强的时间敏感性，因为用户的兴趣往往受到新兴事件、热门话题以及不断变化的现实背景的影响。这些动态特性使得不仅需要建模用户长期稳定的阅读习惯和高阶协作模式（反映他们的长期偏好），还需要捕捉他们短期的、受上下文影响的兴趣变化（随着行为演变而迅速改变）。然而，大多数现有方法依赖于单一静态的交互图，这很难同时捕捉到用户的长期偏好的模式以及随时间快速变化的短期兴趣。 为了解决这一挑战，我们提出了一种统一框架，该框架从全局和局部的时间角度学习用户偏好。其中，一个全局偏好建模组件通过整个交互图捕获长期协作信号；而另一个局部偏好建模组件将历史互动划分为分阶段的时间子图来表示短期动态变化。在局部偏好模块中，LSTM支路用于建模最近兴趣的逐步演变过程，自注意力支路则捕捉长时间跨度内的时间依赖性。 我们在两个大规模的真实世界数据集上进行了广泛的实验，结果表明我们的方法能够持续优于强基线模型，并为各种用户行为和时间设置提供更为新鲜且相关的推荐。
URL
https://arxiv.org/abs/2603.10471
