GTR:可解释的学术文献图形主题感知推荐器

IF 5.9 3区 管理学 Q1 BUSINESS
Ping Ni , Xianquan Wang , Bing Lv , Likang Wu
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引用次数: 0

摘要

在不断扩大的学术论文数字图书馆中,如何在海量的研究论文中找到相关的工作给研究人员带来了巨大的挑战。针对这一问题,我们推出了图形主题感知推荐器(GTR),这是一种专为学术推荐系统定制的创新型端到端深度神经模型。传统方法主要依赖协作过滤、基于内容的过滤和基于图的方法,对科学文档中错综复杂的引用逻辑考虑有限,而 GTR 则不同,它捕捉到了学术网络中固有的细微关系和引用主题。通过利用先进的神经主题建模技术,GTR 将项目到用户的推荐转移到了项目到项目的框架中,从而促进了更准确、更与上下文相关的论文推荐过程。我们的研究通过图神经网络(GNN)利用了学术网络丰富的语境,解决了这些网络中被忽视的差异化语义关系问题。该模型通过有效挖掘关系主题和进行差异化表征来减少信息冗余,从而使 GTR 能够自适应地推断潜在引文主题,增强了模型的可解释性和推荐准确性。此外,GTR 的优化功能还加入了一个新颖的组件池模块,旨在对样本的子图信息进行编码,而无需传统的消息传递,从而提高了模型的效率和可扩展性。通过在多个真实学术数据集上的综合实验,GTR 展示了优于现有先进模型的性能,其推荐具有高准确性和可解释性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GTR: An explainable Graph Topic-aware Recommender for scholarly document

In the ever-expanding digital library of scholarly articles, navigating through the vast amount of available research papers to find relevant work poses a significant challenge to researchers. Addressing this issue, we introduce the Graph Topic-aware Recommender (GTR), an innovative end-to-end deep neural model tailored for scholarly recommendation systems. Unlike traditional methods that primarily rely on Collaborative Filtering, Content-Based Filtering, and Graph-Based approaches with limited consideration of the intricate citing logic within scientific documents, GTR captures the nuanced relationships and citing topics inherent in scholarly networks. By leveraging an advanced neural topic modeling technique, GTR transfers item-to-user recommendation into an item-to-item framework, facilitating a more accurate and contextually relevant paper recommendation process. Our study leverages the contextual richness of scholarly networks through Graph Neural Networks (GNNs), addressing the overlooked aspect of differentiated semantic relationships within these networks. The model stands out by effectively mining relation topics and conducting differentiated representations to minimize information redundancy, which enables GTR to adaptively infer latent citation topics, enhancing the model’s explainability and recommendation accuracy. Besides, the optimization function of GTR incorporates a novel component pooling module, designed to encode the sub-graph information of samples without traditional message passing, thereby improving the model’s efficiency and scalability. Through comprehensive experiments on multiple real-world scholarly datasets, GTR demonstrates superior performance over existing state-of-the-art models, offering both high accuracy and explainability in its recommendations.

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来源期刊
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications 工程技术-计算机:跨学科应用
CiteScore
10.10
自引率
8.30%
发文量
97
审稿时长
63 days
期刊介绍: Electronic Commerce Research and Applications aims to create and disseminate enduring knowledge for the fast-changing e-commerce environment. A major dilemma in e-commerce research is how to achieve a balance between the currency and the life span of knowledge. Electronic Commerce Research and Applications will contribute to the establishment of a research community to create the knowledge, technology, theory, and applications for the development of electronic commerce. This is targeted at the intersection of technological potential and business aims.
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