  With the outbreak of today's streaming data, the sequential recommendation is
a promising solution to achieve time-aware personalized modeling. It aims to
infer the next interacted item of a given user based on the historical item
sequence. Some recent works tend to improve the sequential recommendation via
random masking on the historical item so as to generate self-supervised
signals. But such approaches will indeed result in sparser item sequence and
unreliable signals. Besides, the existing sequential recommendation models are
only user-centric, i.e., based on the historical items by chronological order
to predict the probability of candidate items, which ignores whether the items
from a provider can be successfully recommended. Such user-centric
recommendation will make it impossible for the provider to expose their new
items, failing to consider the accordant interactions between user and item
dimensions. In this paper, we propose a novel Dual Contrastive Network (DCN) to
achieve mutual harmony between user and item provider, generating ground-truth
self-supervised signals for sequential recommendation by auxiliary
user-sequence from an item-centric dimension. Specifically, we propose dual
representation contrastive learning to refine the representation learning by
minimizing the Euclidean distance between the representations of a given
user/item and historical items/users of them. Before the second contrastive
learning module, we perform the next user prediction to capture the trends of
items preferred by certain types of users and provide personalized exploration
opportunities for item providers. Finally, we further propose dual interest
contrastive learning to self-supervise the dynamic interest from the next
item/user prediction and static interest of matching probability. Experiments
on four benchmark datasets verify the effectiveness of our proposed method.
