Chen, Y., Mensah, S. orcid.org/0000-0003-0779-5574, Ma, F. et al. (2 more authors) (2021) Collaborative filtering grounded on knowledge graphs. Pattern Recognition Letters, 151. pp. 55-61. ISSN 0167-8655
Abstract
Matrix Factorization (MF) is a widely used collaborative filtering technique for effectively modeling a user-item interaction in recommender system. Despite the successful application of MF and its variants, the method proves to be effective only in situations where there is an abundance of user-item interactions. However, user-item interaction data are usually sparse, limiting the effectiveness of the method. In addressing this problem, recent methods have proposed to use knowledge graphs (KGs) as additional information to complement the sparse user-item interaction data. This has proved challenging given the complexity of the KG structure. In this paper, we propose a collaborative filtering method that takes advantage of knowledge graphs. More specifically, the embedding of a user and item are both grounded on the item’s attributes in the knowledge graph, and are aggregated with generic user and item representations modeled by MF for implicit recommendation. Our model has demonstrated to outperform the recent state-of-the-art method KGCN [18] in very sparse settings, showing an effective integration of KGs in recommender systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier B.V. |
Keywords: | Collaborative Filtering; Knowledge Graph; Recommender System |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Aug 2021 08:53 |
Last Modified: | 09 Mar 2022 14:16 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.patrec.2021.07.022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176995 |