GCACL-Rec: 一项关于通过全球背景意识和多视角对抗性对抗性联合学习进行对话建议的研究
Xianghui Li1, Xiaowen Liu1, Xinhuan Chen1
1School of Computer Science and Technology, Beihua University, JiLin, China.
PloS one
|October 30, 2025
概括
本研究介绍了GCACL-Rec,这是一种基于会话的新型推模型,可以改善跨会话项目过渡. 该模型增强了用户意图提取和预测准确度,以便提供更好的个性化建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于会话的推 (SBR) 模型使用点击序列来模拟用户行为.
- 现有的SBR方法往往忽略了跨会话的项目过渡,限制了复杂的行为建模.
研究的目的:
- 提出GCACL-Rec模型,该模型包含全球项目过渡信息,用于增强的动态建模.
- 改进SBR中的跨会话依赖模型和用户意图提取.
主要方法:
- 使用多尺度图形神经网络 (MSGNN) 构建了一个多尺度图形结构.
- 引入了一种相对多头注意力机制 (RMA) 用于跨会话依赖模型.
- 采用多视图对比-对抗联合学习策略 (MPACL) 进行有效的用户意图提取.
- 使用混合神经决策森林 (NDF) 和软max结构进行预测.
主要成果:
- 与基准数据集 (Diginetica,Tmall,RetailRocket) 上的现有方法相比,GCACL-Rec表现优越.
- 该模型显示了处理跨会期推任务的明显优势.
结论:
- 通过结合跨会话项目转换,GCACL-Rec有效地模拟复杂的用户行为模式.
- 拟议的模型提高了预测准确性和特征歧视,以改善SBR.
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