Sequence Recommendation for Mobile Application via Time Interval‐Aware Attention and Contrastive Learning

ABSTRACT Mobile application recommendation has emerged as a pivotal domain within the realm of personalized recommendation systems. Traditional mobile application sequence recommendation approaches are predominantly dedicated to the pursuit of sophisticated sequence encoders to achieve more precise representations. However, existing sequence recommendation methods primarily consider the sequential order of historical App interactions, overlooking the time intervals between applications. This oversight hinders the model's capability to fully unearth the temporal correlations in user behavior, consequently limiting the accuracy and personalization of mobile application recommendations. Moreover, the interactions between users and mobile applications are typically sparse, which weakens the model's generalization capabilities. To address these issues, we propose a novel method for mobile application sequence recommendation, incorporating time interval‐aware attention and contrastive learning (called Ti‐CoRe). Specifically, this approach introduces a novel sequence augmentation strategy based on similarity replacement within a contrastive learning framework. By considering textual similarities between applications, this method selectively replaces applications that possess lower similarity scores to generate augmented sequences, increasing the diversity of the sample space and mitigating data sparsity. Furthermore, integrating a time interval‐aware mechanism into the BERT4Rec model, the paper presents a new T‐BERT encoder. It precisely assesses the influence of fluctuating time intervals on the prediction of the subsequent mobile application, thereby ensuring a more nuanced app representation. Experiments conducted on the 360APP real dataset demonstrate that Ti‐CoRe consistently outperforms various baseline models in terms of NDCG and HR metrics.