Improving Sequential Recommendation Consistency with Self-Supervised Imitation

TL;DR

This paper introduces SSI, a self-supervised imitation framework that enhances sequential recommendation models by capturing temporal, persona, and global session consistency, leading to improved prediction accuracy.

Contribution

The paper proposes a novel self-supervised imitation learning approach that integrates multiple consistency signals to improve recommendation quality.

Findings

SSI outperforms state-of-the-art methods on four datasets.

The framework effectively internalizes consistency knowledge.

Self-supervised imitation benefits other recommenders.

Abstract

Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the sequential recommender is prone to make inconsistent predictions. In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation. Precisely, we extract the consistency knowledge by utilizing three self-supervised pre-training tasks, where temporal consistency and persona consistency capture user-interaction dynamics in terms of the chronological order and persona sensitivities, respectively. Furthermore, to provide the model with a global perspective, global session consistency is introduced by maximizing the mutual information among global and local interaction sequences. Finally, to comprehensively take advantage of all three independent aspects of consistency-enhanced knowledge, we establish an integrated imitation learning framework. The consistency knowledge is effectively internalized and transferred to the student model by imitating the conventional prediction logit as well as the consistency-enhanced item representations. In addition, the flexible self-supervised imitation framework can also benefit other student recommenders. Experiments on four real-world datasets show that SSI effectively outperforms the state-of-the-art sequential recommendation methods.