基于文本驱动的神经协同过滤模型用于论文来源追踪。

Text-Driven Neural Collaborative Filtering Model for Paper Source Tracing

July 25, 2024

作者: Aobo Xu, Bingyu Chang, Qingpeng Liu, Ling Jian

cs.AI

摘要

在引文知识图的复杂相互关系中识别重要参考文献是具有挑战性的，这涵盖了通过引文、作者、关键词和其他关联属性建立的连接。Paper Source Tracing（PST）任务旨在利用先进的数据挖掘技术自动识别给定学术文章的关键参考文献。在KDD CUP 2024中，我们设计了一个专为PST任务定制的基于推荐的框架。该框架采用神经协同过滤（NCF）模型生成最终预测。为了处理论文的文本属性并提取模型的输入特征，我们利用了SciBERT，一个预训练语言模型。根据实验结果，我们的方法在平均精度（MAP）指标上达到了0.37814的得分，优于基准模型，并在所有参赛团队中排名第11。源代码可在https://github.com/MyLove-XAB/KDDCupFinal 上公开获取。

English

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.