Text-Driven Neural Collaborative Filtering Model for Paper Source
  Tracing
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.
66a413968069e3c30e131b54
Aobo Xu
admin_assigned
2024-07-30T14:45:52.014Z
63f33a30520c1461892485af
/avatars/77629572bd94f07b2efffa27b1a3a836.svg
Aobo Xu
user
MyloveAB
66a413968069e3c30e131b55
Bingyu Chang
66a413968069e3c30e131b56
Qingpeng Liu
66a413968069e3c30e131b57
Ling Jian
Text-Driven Neural Collaborative Filtering Model for Paper Source
  Tracing
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.
A recommendation-based framework using Neural Collaborative Filtering and SciBERT for Paper Source Tracing outperforms baselines in the KDD CUP 2024, achieving a high Mean Average Precision score.
Neural Collaborative Filtering SciBERT Paper Source Tracing Mean Average Precision