S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

Abstract

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. CCS CONCEPTS • Information systems → Recommender systems. † Equal contribution.

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

“…As the research of self-supervised learning is still in its infancy, there are only several works combining it with recommender systems [24,44,45,64]. These efforts either mine self-supervision signals from future/surrounding sequential data [24,45], or mask attributes of items/users to learn correlations of the raw data [64]. However, these thoughts cannot be easily adopted to social recommendation where temporal factors and attributes may not be available.…”

Relation Representation Learning via Signed Graph Mutual Information Maximization for Trust Prediction

“…However, the transitivity assumption of the messages is not always valid due to the existence of negative edges. Inspired by recent self-learning work with MI maximization [57,58] and knowledge graph embedding (KGE) [59][60][61], we propose a relation representation learning framework via signed graph mutual information maximization, and then use learned vector representation as the input of neural networks to perform the task of trust prediction.…”

Efficient Graph Collaborative Filtering via Contrastive Learning

“…Considering the effectiveness of contrastive learning in various fileds, recently constrastive learning has also introduced into recommender systems for learning the accurate representation of users and items. For instance, Zhou et al propose to maximize the mutual information between different forms or granularities of the item sequence to enhance the item representation learning for improving the sequential recommendation task [27]. Moreover, Xie et al propose to enhance the supervised learning with constrastive learning in a pre-training way, which extracts the self-supervision signals by contrasting the same item sequence from various views generated by different augmentation methods [28].…”