  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.
