Recommender systems filter out information that meets user interests. However, users may be tired of the recommendations that are too similar to the content they have been exposed to in a short historical period, which is the so-called user fatigue. Despite the significance for a better user experience, user fatigue is seldom explored by existing recommenders. In fact, there are three main challenges to be addressed for modeling user fatigue, including what features support it, how it influences user interests, and how its explicit signals are obtained. In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec). To address the first challenge, based on a multi-interest framework, we connect the target item with historical items and construct an interest-aware similarity matrix as features to support fatigue modeling. Regarding the second challenge, built upon feature cross, we propose a fatigue-enhanced multi-interest fusion to capture long-term interest. In addition, we develop a fatigue-gated recurrent unit for short-term interest learning, with temporal fatigue representations as important inputs for constructing update and reset gates. For the last challenge, we propose a novel sequence augmentation to obtain explicit fatigue signals for contrastive learning. We conduct extensive experiments on real-world datasets, including two public datasets and one large-scale industrial dataset. Experimental results show that FRec can improve AUC and GAUC up to 0.026 and 0.019 compared with state-of-the-art models, respectively. Moreover, large-scale online experiments demonstrate the effectiveness of FRec for fatigue reduction. Our codes are released at https://github.com/tsinghua-fib-lab/SIGIR24-FRec.

翻译：推荐系统能够筛选出符合用户兴趣的信息。然而，用户可能对在较短历史时期内接触过的、内容过于相似的推荐感到厌倦，此即所谓的用户疲劳。尽管对于提升用户体验至关重要，但现有推荐系统很少探索用户疲劳问题。实际上，建模用户疲劳需解决三个主要挑战：支撑其特征的因素是什么、它如何影响用户兴趣，以及如何获取其显式信号。本文提出在序列推荐（FRec）的兴趣学习中建模用户疲劳度。针对第一个挑战，基于多兴趣框架，我们将目标项目与历史项目关联，并构建一个兴趣感知的相似度矩阵作为特征以支持疲劳建模。关于第二个挑战，在特征交叉的基础上，我们提出一种疲劳增强的多兴趣融合方法以捕捉长期兴趣。此外，我们开发了一种疲劳门控循环单元用于短期兴趣学习，其中时序疲劳表征作为构建更新门和重置门的重要输入。针对最后一个挑战，我们提出一种新颖的序列增强方法，以获取用于对比学习的显式疲劳信号。我们在真实世界数据集上进行了广泛实验，包括两个公共数据集和一个大规模工业数据集。实验结果表明，与最先进的模型相比，FRec 可将 AUC 和 GAUC 分别提升高达 0.026 和 0.019。此外，大规模在线实验证明了 FRec 在降低疲劳度方面的有效性。我们的代码发布于 https://github.com/tsinghua-fib-lab/SIGIR24-FRec。