Text-Driven Neural Collaborative Filtering Model for Paper Source
  Tracing
Authors: name: Aobo Xu, name: Bingyu Chang, name: Qingpeng Liu, name: Ling Jian

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 2024, 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.