Journal Article10.1007/s10489-021-02872-8
Improving recommender system via knowledge graph based exploring user preference
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TL;DR: An end-to-end framework to improve the recommender system via a knowledge graph based on fusing entity relation(KGFER), which can sufficiently capture the users’ preferences and outperforms state-of-the-art baselines.
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About: This article is published in Applied Intelligence. The article was published on 11 Jan 2022. The article focuses on the topics: Computer science & Computer science.
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Citations
Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding
Zeinab Shokrzadeh,Mohammad-Reza Feizi-Derakhshi,Mohammad Ali Balafar,Jamshid Bagherzadeh Mohasefi +3 more
TL;DR: In this paper , a novel architecture is used with the utilization of pre-trained knowledge graph embeddings of different approaches, which consists of several stages that have various advantages, such as the first step of the proposed method, a knowledge graph from data is created, since multi-hop neighbors in this graph address the ambiguity and redundancy problems.
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A review of recommender systems based on knowledge graph embedding
Jin-cheng Zhang,Azlan Mohd Zain,Kai Zhou,Xi Chen,Ren-Min Zhang +4 more
TL;DR: This review systematically surveys recommender systems leveraging knowledge graph embedding to address sparse interaction data and cold-start problems, covering methods, applications, and datasets, with a focus on incorporating rich knowledge graph information.
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User-station attention inference using smart card data: a knowledge graph assisted matrix decomposition model
TL;DR: Zhang et al. as discussed by the authors introduced the concept of user-station attention, which describes the user's (or user group's) interest in or dependency on specific stations, and developed a matrix decomposition method capturing simultaneously user similarity and station-station relationships using knowledge graphs.
Mixed-curvature knowledge-enhanced graph contrastive learning for recommendation
Yihao Zhang,Junlin Zhu,Ruizhen Chen,Wei-Xing Liao,Yulin Wang +4 more
TL;DR: This paper proposes MKGCL, a mixed-curvature knowledge-enhanced graph contrastive learning framework for recommendation, leveraging knowledge graphs to generate reliable regulatory signals and accommodating varying graph structures through product manifolds in a curvature space.
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Dual channel group-aware graph convolutional networks for collaborative filtering
Jinsong Zhao,Kaiwen Huang,Ping Li +2 more
- 09 Aug 2023
TL;DR: A dual channel group-aware graph convolution model, called DG-GCN, which first performs message passing on the user-item interaction graph to leverage the direct and higher-order connectivity information for further grouping, and then groups users and items separately through dual group-aware modules based on their latent interests and categories.
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