Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation

TL;DR

This paper introduces LMA4Rec, a novel self-supervised learning approach for sequential recommendation that employs learnable model augmentation to generate contrastive views, improving recommendation accuracy.

Contribution

It proposes a learnable model augmentation technique using Bernoulli dropout for self-supervised learning in sequential recommendation, addressing limitations of uniform data augmentation.

Findings

LMA4Rec outperforms baseline methods on three public datasets.

Learnable model augmentation enhances sequence correlation preservation.

Self-supervised signals improve recommendation accuracy.

Abstract

Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. However, most of existing SSL methods use a uniform data augmentation scheme, which loses the sequence correlation of an original sequence. To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes model augmentation as a supplementary method for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli dropout to implement model augmentation learnable operations. Next, self-supervised learning is used between the contrastive views to extract self-supervised signals from an original sequence. Finally, experiments on three public datasets show that the LMA4Rec method effectively improves sequential recommendation performance compared with baseline methods.