Intent Propagation Contrastive Collaborative Filtering

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 understanding of interactions between nodes. We also develop an intent message propagation method that incorporates graph structure information into the disentanglement process, thereby expanding the consideration scope of disentanglement. In addition, contrastive learning techniques are employed to align node representations derived from structure and intents, providing direct supervision for the disentanglement process, mitigating biases, and enhancing the model's robustness to overfitting. Experiments on three real data graphs illustrate the superiority of the proposed approach.