Neural Causal Graph Collaborative Filtering

TL;DR

This paper introduces NCGCF, a novel approach combining causal modeling with graph convolutional networks to improve recommendation accuracy by capturing complex causal relationships in user-item interactions.

Contribution

The paper proposes a new neural causal graph model for collaborative filtering that effectively models complex causal dependencies, enhancing recommendation quality over existing GCN-based methods.

Findings

NCGCF outperforms baseline models in recommendation accuracy

The causal modeling approach captures complex node dependencies

Extensive experiments validate the effectiveness of the proposed method

Abstract

Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn user and item embeddings with Graph Convolutional Network (GCN) and utilize these embeddings for CF models. However, existing GCN-based methods are insufficient in generating satisfactory embeddings for CF models. This is because they fail to model complex node dependencies and variable relation dependencies from a given graph, making the learned embeddings fragile to uncover the root causes of user interests. In this work, we propose to integrate causal modeling with the learning process of GCN-based GCF models, leveraging causality-aware graph embeddings to capture complex causal relations in recommendations. We complete the task by 1) Causal Graph conceptualization, 2) Neural Causal Model parameterization and 3) Variational inference for Neural Causal Model. Our Neural Causal Model, called Neural Causal Graph Collaborative Filtering (NCGCF), enables causal modeling for GCN-based GCF to facilitate accurate recommendations. Extensive experiments show that NCGCF provides precise recommendations that align with user preferences. We release our code and processed datasets at https://github.com/Chrystalii/CNGCF.