针对引文推荐系统的进化知识图谱表示学习与多重关注策略

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jhih-Chen Liu, Chiao-Ting Chen, Chi Lee, Szu-Hao Huang
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引用次数: 0

摘要

人工智能领域的论文数量日益增多,这凸显了研究人员提高搜索相关文章效率的必要性。大多数论文推荐模型要么依赖于论文之间简单的引用关系,要么专注于基于内容的方法,这两种方法都忽略了学术网络内部的互动。为了解决上述问题,知识图嵌入(KGE)方法被用于引文推荐,因为最近的研究证明图表示法可以有效提高推荐模型的准确性。然而,学术网络是动态的,随着时间的推移,用户和项目的表征会发生变化。大多数基于知识图谱的引文推荐主要是针对静态图谱设计的,因此无法捕捉动态知识图谱(DKG)结构的演变。为了应对这些挑战,我们引入了演化知识图嵌入(EKGE)方法。在这种方法中,不断演化的知识图谱被输入到时间序列模型中,以学习结构演化的模式。该模型能够在不同的时间点为每个实体生成嵌入,从而克服了静态模型需要重新训练以获取每个特定时间点的嵌入的局限性。为了提高特征提取的效率,我们采用了多重关注策略。这有助于模型找到与用户需求密切相关的推荐列表,从而提高推荐准确率。在引文推荐数据集上进行的各种实验表明,与其他 KGE 方法相比,EKGE 模型的预测准确率提高了 1.13%。此外,通过加入关注机制,该模型的准确率还能再提高 0.84%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System

The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem, knowledge graph embedding (KGE) methods have been used for citation recommendations because recent research proving that graph representations can effectively improve recommendation model accuracy. However, academic networks are dynamic, leading to changes in the representations of users and items over time. The majority of KGE-based citation recommendations are primarily designed for static graphs, thus failing to capture the evolution of dynamic knowledge graph (DKG) structures. To address these challenges, we introduced the evolving knowledge graph embedding (EKGE) method. In this methodology, evolving knowledge graphs are input into time-series models to learn the patterns of structural evolution. The model has the capability to generate embeddings for each entity at various time points, thereby overcoming limitation of static models that require retraining to acquire embeddings at each specific time point. To enhance the efficiency of feature extraction, we employed a multiple attention strategy. This helped the model find recommendation lists that are closely related to a user’s needs, leading to improved recommendation accuracy. Various experiments conducted on a citation recommendation dataset revealed that the EKGE model exhibits a 1.13% increase in prediction accuracy compared to other KGE methods. Moreover, the model’s accuracy can be further increased by an additional 0.84% through the incorporation of an attention mechanism.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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