Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation

arXiv:2404.11993v1 Announce Type: cross
Abstract: 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 …

abstract arxiv behavior cart cs.ir cs.lg current differences diverse explore knowledge perspective purchase recommendation relations studies type view