  Sequential recommendation (SR) aims to predict the subsequent behaviors of
users by understanding their successive historical behaviors. Recently, some
methods for SR are devoted to alleviating the data sparsity problem (i.e.,
limited supervised signals for training), which take account of contrastive
learning to incorporate self-supervised signals into SR. Despite their
achievements, it is far from enough to learn informative user/item embeddings
due to the inadequacy modeling of complex collaborative information and
co-action information, such as user-item relation, user-user relation, and
item-item relation. In this paper, we study the problem of SR and propose a
novel multi-level contrastive learning framework for sequential recommendation,
named MCLSR. Different from the previous contrastive learning-based methods for
SR, MCLSR learns the representations of users and items through a cross-view
contrastive learning paradigm from four specific views at two different levels
(i.e., interest- and feature-level). Specifically, the interest-level
contrastive mechanism jointly learns the collaborative information with the
sequential transition patterns, and the feature-level contrastive mechanism
re-observes the relation between users and items via capturing the co-action
information (i.e., co-occurrence). Extensive experiments on four real-world
datasets show that the proposed MCLSR outperforms the state-of-the-art methods
consistently.
