Journal
ACM TRANSACTIONS ON INFORMATION SYSTEMS
Volume 40, Issue 3, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3473973
Keywords
Explainable citation recommendation; graph neural networks; relational topic models; collaborative filtering
Categories
Funding
- CSC Scholarship by China Scholarship Council
- MindSpore
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This article proposes a model called Graph Neural Collaborative Topic Model which combines the advantages of relational topic models and graph neural networks to capture high-order citation relationships and achieve higher explainability. Experimental results demonstrate that the model outperforms competitive methods in citation recommendation and is able to learn better topics.
Due to the overload of published scientific articles, citation recommendation has long been a critical research problem for automatically recommending the most relevant citations of given articles. Relational topic models (RTMs) have shown promise on citation prediction via joint modeling of document contents and citations. However, existing RTMs can only capture pairwise or direct (first-order) citation relationships among documents. The indirect (high-order) citation links have been explored in graph neural network-based methods, but these methods suffer from the well-known explainability problem. In this article, we propose a model called Graph Neural Collaborative Topic Model that takes advantage of both relational topic models and graph neural networks to capture high-order citation relationships and to have higher explainability due to the latent topic semantic structure. Experiments on three real-world citation datasets show that our model outperforms several competitive baseline methods on citation recommendation. In addition, we show that our approach can learn better topics than the existing approaches. The recommendation results can be well explained by the underlying topics.
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