Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior
  Recommendation
Shunpan Liang Junjie Zhao Chen Li Yu Lei
Multi-behavioral recommendation optimizes user experiences by providing users
with more accurate choices based on their diverse behaviors, such as view, add
to cart, and purchase. Current studies on multi-behavioral recommendation
mainly explore the connections and differences between multi-behaviors from an
implicit perspective. Specifically, they directly model those relations using
black-box neural networks. In fact, users' interactions with items under
different behaviors are driven by distinct intents. For instance, when users
view products, they tend to pay greater attention to information such as
ratings and brands. However, when it comes to the purchasing phase, users
become more price-conscious. To tackle this challenge and data sparsity problem
in the multi-behavioral recommendation, we propose a novel model:
Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) model. This model
uses relationships in the knowledge graph to construct intents, aiming to mine
the connections between users' multi-behaviors from the perspective of intents
to achieve more accurate recommendations. KAMCL is equipped with two
contrastive learning schemes to alleviate the data scarcity problem and further
enhance user representations. Extensive experiments on three real datasets
demonstrate the superiority of our model.