孔亚钰;卢玉洁;孙中天;肖敬先;侯昊辰;陈廷伟;

• 1:辽宁大学信息学院
• 2:杜伦大学

摘要(Abstract)：

在基于会话的推荐中，与传统序列建模相比，将会话序列建模为图结构在该领域表现得更为出色。但是，现有的研究方法仅利用图结构来挖掘项目之间转换特性，以此捕获用户当前兴趣的能力有限。本文提出一种面向强化当前兴趣的图神经网络推荐算法，通过引入位置嵌入，并与图神经网络相结合，从而互补顺序感知模型和图形感知模型的优势。会话序列被建模为图结构，并取原始序列的最后一次点击，通过多头注意力机制计算其对图节点信息的注意力权重，以更加准确地获取用户当前兴趣的表示。同时，在2个真实的数据集上进行验证实验，结果表明本文提出的方法实现了所有方法的最佳性能，特别是在Diginetica数据集上，所有评价指标都提升了7%以上，MRR@10指标甚至提升了9.52%,证明本文所提方法对基于会话推荐的正确性和有效性。

关键词(KeyWords)： 基于会话的推荐;会话图;图神经网络;多头注意力机制;位置嵌入

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(61802160)

作者(Authors): 孔亚钰;卢玉洁;孙中天;肖敬先;侯昊辰;陈廷伟;

DOI: 10.16088/j.issn.1001-6600.2021071405

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