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Learning Fine-Grained User Preference for Personalized Recommendation

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PRE
AI
M
Mingxing Zhang
X
Xiaoxiong Zhang
王
王帅 (Shuai Wang)
G
Guohua Wu
DOI：10.26599/TST.2024.9010216delete
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摘要

摘要

En 中文
知识图谱（KGs）作为辅助信息在推荐系统中已引起广泛关注。大多数现有研究将物品视为知识图谱中的实体，并利用图神经网络学习物品表示。然而，这些算法存在两个挑战：1）它们提供了推荐结果，但未能解释用户偏好这些推荐的原因；2）用户向量表示集中在小区域内，导致产生相似的批量推荐。本研究聚焦于通过用户-物品交互学习细粒度用户偏好（LFUP），并利用知识图谱捕捉用户与物品交互的原因。此外，通过优化用户在向量空间中的分布来实现个性化推荐任务。用户偏好通过用户相关的历史交互物品及知识图谱中的重要关系进行建模。随后，聚合两种视图的信息以减少它们之间的语义差异。最后，通过对比学习最大化不同用户表示之间的空间距离来个性化用户偏好。在公开数据集上的实验证明，LFUP显著提升了用户偏好建模和个性化推荐的效果。
Keyword:
graph neural network
knowledge graph
personalized recommendation
recommendation systems

期刊

T
Tsinghua Science and Technology
IF:
3.5
论文数:
987
被引数:
2.5K

机构

N
National University of Defense Technology
学者数:
3.3K
论文数: 1.0K
被引数: 8.2K
C
Central South University
学者数:
10.0W
论文数: 7.2W
被引数: 10.9W
U
university of alberta
学者数:
5.1W
论文数: 4.9W
被引数: 65
S
Southeast University
学者数:
2.1W
论文数: 8.6K
被引数: 480
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