Intent Propagation Contrastive Collaborative Filtering
Haojie Li, Junwei Du, Guanfeng Liu, Feng Jiang, Yan Wang, Xiaofang Zhou

TL;DR
The paper introduces IPCCF, a novel collaborative filtering method that enhances interpretability and recommendation accuracy by integrating deep semantic intent propagation, graph structure, and contrastive learning.
Contribution
It proposes a double helix message propagation framework and intent message propagation with contrastive learning for improved disentanglement in recommendation systems.
Findings
Outperforms existing methods on three real-world datasets.
Effectively captures deep semantic information of nodes.
Enhances robustness and reduces overfitting in recommendations.
Abstract
Disentanglement techniques used in collaborative filtering uncover interaction intents between nodes, improving the interpretability of node representations and enhancing recommendation performance. However, existing disentanglement methods still face two problems. First, they focus on local structural features derived from direct node interactions and overlook the comprehensive graph structure, which limits disentanglement accuracy. Second, the disentanglement process depends on backpropagation signals derived from recommendation tasks and lacks direct supervision, which may lead to biases and overfitting. To address these issues, we propose the Intent Propagation Contrastive Collaborative Filtering (IPCCF) algorithm. Specifically, we design a double helix message propagation framework to more effectively extract the deep semantic information of nodes, thereby improving the model's…
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