  Recommending cold-start items is a long-standing and fundamental challenge in
recommender systems. Without any historical interaction on cold-start items, CF
scheme fails to use collaborative signals to infer user preference on these
items. To solve this problem, extensive studies have been conducted to
incorporate side information into the CF scheme. Specifically, they employ
modern neural network techniques (e.g., dropout, consistency constraint) to
discover and exploit the coalition effect of content features and collaborative
representations. However, we argue that these works less explore the mutual
dependencies between content features and collaborative representations and
lack sufficient theoretical supports, thus resulting in unsatisfactory
performance. In this work, we reformulate the cold-start item representation
learning from an information-theoretic standpoint. It aims to maximize the
mutual dependencies between item content and collaborative signals.
Specifically, the representation learning is theoretically lower-bounded by the
integration of two terms: mutual information between collaborative embeddings
of users and items, and mutual information between collaborative embeddings and
feature representations of items. To model such a learning process, we devise a
new objective function founded upon contrastive learning and develop a simple
yet effective Contrastive Learning-based Cold-start Recommendation
framework(CLCRec). In particular, CLCRec consists of three components:
contrastive pair organization, contrastive embedding, and contrastive
optimization modules. It allows us to preserve collaborative signals in the
content representations for both warm and cold-start items. Through extensive
experiments on four publicly accessible datasets, we observe that CLCRec
achieves significant improvements over state-of-the-art approaches in both
warm- and cold-start scenarios.
