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

TL;DR

This paper introduces KAMCL, a knowledge-aware contrastive learning model that captures user intents from multi-behavior data using knowledge graphs, improving recommendation accuracy and addressing data sparsity.

Contribution

It proposes a novel multi-intent contrastive learning framework leveraging knowledge graphs to better model user behaviors and intents in multi-behavior recommendation systems.

Findings

KAMCL outperforms existing models on three real datasets.

The model effectively alleviates data sparsity issues.

Knowledge-aware intent modeling enhances recommendation accuracy.

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 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.