title: Text-Driven Neural Collaborative Filtering Model for Paper Source
    Tracing
summary: 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.
paper_authors._id: 66a413968069e3c30e131b54
paper_authors.name: Aobo Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-30T14:45:52.014Z
paper_authors._id: 63f33a30520c1461892485af
paper_authors.avatarUrl: /avatars/77629572bd94f07b2efffa27b1a3a836.svg
paper_authors.fullname: Aobo Xu
paper_authors.type: user
paper_authors.user: MyloveAB
paper_authors._id: 66a413968069e3c30e131b55
paper_authors.name: Bingyu Chang
paper_authors._id: 66a413968069e3c30e131b56
paper_authors.name: Qingpeng Liu
paper_authors._id: 66a413968069e3c30e131b57
paper_authors.name: Ling Jian
paper_title: Text-Driven Neural Collaborative Filtering Model for Paper Source
    Tracing
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Neural Collaborative Filtering SciBERT Paper Source Tracing Mean Average Precision