文章基本信息

标题：An Academic Text Recommendation Method Based on Graph Neural Network
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作者：Jie Yu ; Chenle Pan ; Yaliu Li 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：4
页码：172
DOI：10.3390/info12040172
出版社：MDPI Publishing
摘要：Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.
关键词：session-based recommendation; text recommendation; graph neural network; attention mechanism session-based recommendation ; text recommendation ; graph neural network ; attention mechanism