A well-informed recommendation framework could not only help users identify
their interested items, but also benefit the revenue of various online
platforms (e.g., e-commerce, social media). Traditional recommendation models
usually assume that only a single type of interaction exists between user and
item, and fail to model the multiplex user-item relationships from multi-typed
user behavior data, such as page view, add-to-favourite and purchase. While
some recent studies propose to capture the dependencies across different types
of behaviors, two important challenges have been less explored: i) Dealing with
the sparse supervision signal under target behaviors (e.g., purchase). ii)
Capturing the personalized multi-behavior patterns with customized dependency
modeling. To tackle the above challenges, we devise a new model CML,
Contrastive Meta Learning (CML), to maintain dedicated cross-type behavior
dependency for different users. In particular, we propose a multi-behavior
contrastive learning framework to distill transferable knowledge across
different types of behaviors via the constructed contrastive loss. In addition,
to capture the diverse multi-behavior patterns, we design a contrastive meta
network to encode the customized behavior heterogeneity for different users.
Extensive experiments on three real-world datasets indicate that our method
consistently outperforms various state-of-the-art recommendation methods. Our
empirical studies further suggest that the contrastive meta learning paradigm
offers great potential for capturing the behavior multiplicity in
recommendation. We release our model implementation at:
this https URL.

该研究提出了一种新的模型 CML，即对比元学习，用于处理具有不同类型行为的多行为数据中用户和物品之间的关系，通过对比损失获取可传输知识，以解决传统推荐模型中存在的个性化多行为模式和匮乏监督者信号的问题。该方法在三个真实世界数据集上表现出了卓越的性能，并释放出模型实现。