S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

by   Kun Zhou, et al.

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance.


page 1

page 2

page 3

page 4


Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation

Sequential Recommendation aims to predict the next item based on user be...

Improving Sequential Recommendation Consistency with Self-Supervised Imitation

Most sequential recommendation models capture the features of consecutiv...

Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of seq...

Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation

Self-supervised sequential recommendation significantly improves recomme...

ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

The ability to detect Out-of-Domain (OOD) inputs has been a critical req...

Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

Transformer and its variants are a powerful class of architectures for s...

Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation

Self-supervised learning (SSL) recently has achieved outstanding success...

Please sign up or login with your details

Forgot password? Click here to reset