Time-aware Path Reasoning on Knowledge Graph for Recommendation

被引:23
|
作者
Zhao, Yuyue [1 ]
Wang, Xiang [1 ]
Chen, Jiawei [1 ]
Wang, Yashen [2 ]
Tang, Wei [1 ]
He, Xiangnan [1 ]
Xie, Haiyong [3 ,4 ,5 ]
机构
[1] Univ Sci & Technol China, 96 JinZhai Rd, Hefei, Peoples R China
[2] China Acad Elect & Informat Technol CETC, Natl Engn Lab Risk Percept & Prevent RPP, Key Lab Cognit & Intelligence Technol CETC CIT, Informat Sci Acad CETC, 11 ShuangYuan Rd, Beijing 100041, Peoples R China
[3] Capital Med Univ, Key Lab Cyberculture Content Cognit & Detect CCCD, MCT, 96 JinZhai Rd, Hefei, Peoples R China
[4] Capital Med Univ, Adv Innovat Ctr Human Brain Protect, 96 JinZhai Rd, Hefei, Peoples R China
[5] Univ Sci & Technol China, CCCD Key Lab MCT, Hefei, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Explainable recommendation; temporal knowledge graphs; reinforcement learning;
D O I
10.1145/3531267
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to its ability of providing explicit explanations. However, current KG-based explainable recommendation methods unfortunately ignore the temporal information (such as purchase time, recommend time, etc.), which may result in unsuitable explanations. In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations. First, we present an efficient time-aware interaction relation extraction component to construct collaborative knowledge graph with time-aware interactions (TCKG for short), and then we introduce a novel time-aware path reasoning method for recommendation. We conduct extensive experiments on three real-world datasets. The results demonstrate that the proposed TPRec could successfully employ TCKG to achieve substantial gains and improve the quality of explainable recommendation.
引用
收藏
页数:26
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