基於文本的神經協同過濾模型用於論文來源追蹤

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

在引文知識圖中識別重要參考文獻是具有挑戰性的，該知識圖涵蓋了透過引文、作者、關鍵詞和其他關聯屬性建立的連結。論文來源追蹤（PST）任務旨在利用先進的數據挖掘技術自動識別學術文章的關鍵參考文獻。在 KDD CUP 2024 中，我們設計了一個針對 PST 任務量身定制的基於推薦的框架。該框架採用神經協同過濾（NCF）模型生成最終預測。為了處理論文的文本屬性並提取模型的輸入特徵，我們使用了 SciBERT，一個預先訓練的語言模型。根據實驗結果，我們的方法在平均精度（MAP）指標上達到了 0.37814 的得分，優於基準模型，並在所有參賽團隊中排名第 11。源代碼可在 https://github.com/MyLove-XAB/KDDCupFinal 公開獲取。

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.