Journal Article10.1007/S10489-021-02363-W
Personalized recommendation system based on knowledge embedding and historical behavior
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TL;DR: This paper regards knowledge graphs as heterogeneous networks to add auxiliary information, proposes a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior andknowledge graphs to provide more accurate and diverse recommendations to the users.
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Abstract: Collaborative filtering (CF) usually suffers from limited performance in recommendation systems due to the sparsity of user–item interactions and cold start problems. To address these issues, auxiliary information from knowledge graphs, such as social networks and item properties, is typically used to boost performance. The current recommended algorithms based on knowledge graphs fail to utilize rich semantic associations. In this paper, we regard knowledge graphs as heterogeneous networks to add auxiliary information, propose a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. Our proposed ReBKC shows a significant improvement on three datasets compared to state-of-the-art methods. These results verify the effectiveness of learning short-term and long-term user preferences from their historical behavior and by integrating knowledge graphs to deeply identify user preferences.
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Citations
Micro-Directional Propagation Method Based on User Clustering
TL;DR: This paper has improved the traditional collaborative filtering method and proposed a collaborative filtering method based on user clustering that greatly reduces the recommendation time, and effectively solves the cold start problem in micro directional propagation.
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AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder
TL;DR: Zhang et al. as discussed by the authors proposed a knowledge graph recommendation system algorithm for the multiple paths RNN encoder (AGRE), which fully considers the association between paths and achieved good results in terms of AUC and Precision@K.
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A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
TL;DR: In this article , the authors explored the current state of research on federated learning for recommendation systems, highlighting existing research issues and possible solutions, and examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration.
Improving recommender system via knowledge graph based exploring user preference
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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Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation
TL;DR: In this article , the authors proposed a novel KGEE (Knowledge Graph Embedding Enhancement) approach of Hashing-based Semantic-relevance Attributed Graph-embedding Enhancement (H-SAGE) to model semantically-relevant higher-order entities and relations into the unique Meta-paths.
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References
Graph Attention Networks
Petar Veličković,Guillem Cucurull,Arantxa Casanova,Adriana Romero,Pietro Liò,Yoshua Bengio +5 more
- 15 Feb 2018
TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
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TL;DR: TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.
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Zhen Wang,Jianwen Zhang,Jianlin Feng,Zheng Chen +3 more
- 27 Jul 2014
TL;DR: This paper proposes TransH which models a relation as a hyperplane together with a translation operation on it and can well preserve the above mapping properties of relations with almost the same model complexity of TransE.
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