  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.
