QAGCF: Graph Collaborative Filtering for Q A Recommendation
CoRR(2024)
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.
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