Time-aware Path Reasoning on Knowledge Graph for Recommendation

Author:

Zhao Yuyue1ORCID,Wang Xiang1ORCID,Chen Jiawei1ORCID,Wang Yashen2ORCID,Tang Wei1ORCID,He Xiangnan1ORCID,Xie Haiyong3ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. National Engineering Laboratory for Risk Perception and Prevention (RPP), Beijing, China

3. Key Laboratory of Cyberculture Content Cognition and Detection, Ministry of Culture and Tourism, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Hefei, China

Abstract

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.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

USTC Research Funds of the Double First-Class Initiative

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Science Applications,General Business, Management and Accounting,Information Systems

Reference71 articles.

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