Explainable Reasoning over Knowledge Graphs for Recommendation
Open accessAbstract
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.In this paper, we contribute a new model named Knowledgeaware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.
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Application scenarios such as smart parking could benefit from recent advances in causal inference and discovery, graph-based reasoning, reinforcement learning, and speech recognition and synthesis.
“These application scenarios could benefit from recent advances in causal inference and discovery [39], graphbased reasoning [40], reinforcement learning [41], and speech recognition and synthesis [24], [42].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [40]
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- Authors
- Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T.-S.
- Affiliations
- National University of Singapore (SG)
- eBay (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 5329-5336
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2966349618
- Cited by
- 785
- References
- 26
- Citations by year
- 2026: 29 · 2025: 94 · 2024: 134 · 2023: 153 · 2022: 136 · 2021: 115 · 2020: 92 · 2019: 32
- FWCI
- 74.08
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- National University of Singapore (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation
- National Research Foundation Singapore (AISG-100E-2018-002)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- explainable recommendation, knowledge graphs, path reasoning, user preference inference, path representation, sequential dependencies, movie recommendation, music recommendation
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