QAGCF: Graph Collaborative Filtering for Q&A Recommendation

TL;DR

This paper introduces QAGCF, a graph neural network model that disentangles collaborative and semantic information in question-answer pairs for improved Q&A recommendation, outperforming existing methods.

Contribution

The paper proposes a novel graph neural network approach with separate collaborative and semantic views, effectively modeling complex user behaviors in Q&A platforms.

Findings

QAGCF outperforms baseline methods on multiple datasets.

The model effectively disentangles collaborative and semantic information.

Contrastive learning enhances embedding robustness.

Abstract

Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one item. This makes user behaviors more complex, and presents two challenges for Q&A recommendation, including: the collaborative information entanglement, which means user feedback is influenced by either the question or the answer; and the semantic information entanglement, where questions are correlated with their corresponding answers, and correlations also exist among different question-answer pairs. Traditional recommendation methods treat the question-answer pair as a whole or only consider the answer as a single item, which overlooks the two challenges and cannot effectively model user interests. To address these challenges, we introduce Question & Answer Graph Collaborative Filtering (QAGCF), a graph neural network model that creates separate graphs for collaborative and semantic views to disentangle the information in question-answer pairs. The collaborative view disentangles questions and answers to individually model collaborative information, while the semantic view captures the semantic information both within and between question-answer pairs. These views are further merged into a global graph to integrate the collaborative and semantic information. Polynomial-based graph filters are used to address the high heterophily issues of the global graph. Additionally, contrastive learning is utilized to obtain robust embeddings during training. Extensive experiments on industrial and public datasets demonstrate that QAGCF consistently outperforms baselines and achieves state-of-the-art results.