面向冷启动用户的元学习与图转移学习序列推荐

doi:10.6040/j.issn.1672-3961.0.2023.016

摘要/Abstract

摘要： 为解决推荐系统用户冷启动问题,提出面向冷启动用户的元学习与图转移学习序列推荐(sequential recommendation for cold-start users with meta graph transitional learning, MetaGTL)。MetaGTL在不使用其他辅助信息的前提下,采用图神经网络(graph neural network, GNN)建模序列间物品高阶关系生成用户物品嵌入;将交互序列构造为物品对集合,使用序列编码模块捕捉物品间的转移关系,动态建模用户兴趣;采用注意力机制,生成准确的用户特征;采用基于梯度的元学习方法训练模型,生成初始化模型;对模型的工作性能和结果进行详细分析,结合基线模型进行对比评价。试验结果表明,基于元学习与图转移学习的MetaGTL在缺少辅助信息的用户冷启动任务中具有更高的预测精度。

中图分类号:

TP391.3

李璐,张志军,范钰敏,王星,袁卫华. 面向冷启动用户的元学习与图转移学习序列推荐[J]. 山东大学学报 (工学版), 2024, 54(2): 69-79.

LI Lu, ZHANG Zhijun, FAN Yumin, WANG Xing, YUAN Weihua. Sequential recommendation for cold-start users with meta graph transitional learning[J]. Journal of Shandong University(Engineering Science), 2024, 54(2): 69-79.

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