Contrastive learning (CL) has shown its power in recommendation. However,
most CL-based recommendation models build their CL tasks merely focusing on the
user's aspects, ignoring the rich diverse information in items. In this work,
we propose a novel Multi-granularity item-based contrastive learning (MicRec)
framework for the matching stage (i.e., candidate generation) in
recommendation, which systematically introduces multi-aspect item-related
information to representation learning with CL. Specifically, we build three
item-based CL tasks as a set of plug-and-play auxiliary objectives to capture
item correlations in feature, semantic and session levels. The feature-level
item CL aims to learn the fine-grained feature-level item correlations via
items and their augmentations. The semantic-level item CL focuses on the
coarse-grained semantic correlations between semantically related items. The
session-level item CL highlights the global behavioral correlations of items
from users' sequential behaviors in all sessions. In experiments, we conduct
both offline and online evaluations on real-world datasets, verifying the
effectiveness and universality of three proposed CL tasks. Currently, MicRec
has been deployed on a real-world recommender system, affecting millions of
users. The source code will be released in the future.Comment: 17 pages, under revie