Translational models for item recommendation
Translational models have proven to be accurate and efficient at learning entity and relation
representations from knowledge graphs for machine learning tasks such as knowledge …
representations from knowledge graphs for machine learning tasks such as knowledge …
Deep learning on knowledge graph for recommender system: A survey
Recent advances in research have demonstrated the effectiveness of knowledge graphs
(KG) in providing valuable external knowledge to improve recommendation systems (RS). A …
(KG) in providing valuable external knowledge to improve recommendation systems (RS). A …
Knowledge graph enhanced neural collaborative recommendation
Existing neural collaborative filtering (NCF) recommendation methods suffer from severe
sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected …
sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected …
Knowledge-adaptive contrastive learning for recommendation
By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based
recommender systems have shown their superiority in alleviating data sparsity and cold start …
recommender systems have shown their superiority in alleviating data sparsity and cold start …
Knowledge-aware fine-grained attention networks with refined knowledge graph embedding for personalized recommendation
W Wang, X Shen, B Yi, H Zhang, J Liu, C Dai - Expert Systems with …, 2024 - Elsevier
Recommendation systems aim to provide users with personalized and accurate services by
integrating various machine learning technologies. Suffering from the puzzles such as cold …
integrating various machine learning technologies. Suffering from the puzzles such as cold …
AGRE: A knowledge graph recommendation algorithm based on multiple paths embeddings RNN encoder
N Zhao, Z Long, J Wang, ZD Zhao - Knowledge-Based Systems, 2023 - Elsevier
More and more researches have focused on the use of knowledge graphs (KG) to solve the
sparsity problem of traditional collaborative filtering recommendation systems. Most KG …
sparsity problem of traditional collaborative filtering recommendation systems. Most KG …
Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences
Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …
recommendation accuracy and explainability. However, existing methods largely assume …
Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks
Abstract Knowledge graphs (KGs) are increasingly used to solve the data sparsity and cold
start problems of collaborative filtering. Recently, graph neural networks (GNNs) have been …
start problems of collaborative filtering. Recently, graph neural networks (GNNs) have been …
Knowledge graph embeddings with node2vec for item recommendation
In the past years, knowledge graphs have proven to be beneficial for recommender systems,
efficiently addressing paramount issues such as new items and data sparsity. Graph …
efficiently addressing paramount issues such as new items and data sparsity. Graph …
KGTORe: tailored recommendations through knowledge-aware GNN models
Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …
enhance the performance of recommendation algorithms. Their graph-based structure …