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CAMBSRec: A Context-Aware Multi-Behavior Sequential Recommendation Model

delete2025-08-17
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OA
AI
B
Bohan Zhuang
Y
Yan Lan
M
M Zhang *
DOI：10.3390/informatics12030079delete
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摘要

摘要

En 中文
多行为序列推荐（MBSRec）是一种序列推荐形式。它利用用户的历史交互行为类型来更好地预测其下一步动作。该方法的适应性优于传统模型。随着Transformer模型的兴起，注意力机制被广泛应用于推荐算法。然而，它们存在低通滤波问题，且现有模型中简单的可学习位置编码带来的性能提升有限。为解决这些问题，我们提出了基于上下文感知的多行为序列推荐模型（CAMBSRec）。该模型分别对物品和行为类型进行编码，用上下文相似性位置编码替换传统位置编码，并应用离散傅里叶变换分离高低频分量并增强高频分量，以抵消低通滤波效应。在三个公开数据集上的实验表明，CAMBSRec优于五个基线模型，展示了其在推荐性能方面的优势。
Keyword:
multi-behavior sequential recommendation
transformer model
attention mechanism
positional encoding
Fourier transform

期刊

I
Informatics
IF:
0
论文数:
230
被引数:
0

机构

D
dalian minzu university
学者数:
595
论文数: 228
被引数: 0
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