Modeling User Fatigue for Sequential Recommendation

TL;DR

This paper introduces FRec, a novel model for sequential recommendation that explicitly captures user fatigue through interest-aware features, fatigue-enhanced interest fusion, and fatigue signals, improving recommendation accuracy and user experience.

Contribution

FRec is the first model to explicitly incorporate user fatigue signals into interest learning for sequential recommendation, addressing key challenges with innovative feature construction and sequence augmentation.

Findings

FRec improves AUC and GAUC by up to 0.026 and 0.019 over state-of-the-art models.

FRec effectively reduces user fatigue in online recommendation settings.

Experimental results validate the superiority of FRec on multiple datasets.

Abstract

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.