Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing
This work addresses the challenge of identifying pivotal references in citation graphs for researchers, but it is incremental as it adapts existing methods to a specific task.
The paper tackled the Paper Source Tracing (PST) task by developing a recommendation-based framework using Neural Collaborative Filtering (NCF) and SciBERT for text processing, achieving a MAP score of 0.37814 and ranking 11th in the KDD CUP OAG-Challenge.
Identifying significant references within the complex interrelations of a citation knowledge graph is challenging, which encompasses connections through citations, authorship, keywords, and other relational attributes. The Paper Source Tracing (PST) task seeks to automate the identification of pivotal references for given scholarly articles utilizing advanced data mining techniques. In the KDD CUP OAG-Challenge PST track, we design a recommendation-based framework tailored for the PST task. This framework employs the Neural Collaborative Filtering (NCF) model to generate final predictions. To process the textual attributes of the papers and extract input features for the model, we utilize SciBERT, a pre-trained language model. According to the experimental results, our method achieved a score of 0.37814 on the Mean Average Precision (MAP) metric, outperforming baseline models and ranking 11th among all participating teams. The source code is publicly available at https://github.com/MyLove-XAB/KDDCupFinal.