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Knowledge Graph Embeddings with node2vec for Item Recommendation
[chapter]
2018
Lecture Notes in Computer Science
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. At the same time, several works have recently tackled the problem of knowledge graph completion through machine learning algorithms able to learn knowledge graph embeddings. In this paper, we show that the item recommendation problem can be seen as a specific case of knowledge graph completion problem, where the "feedback"
doi:10.1007/978-3-319-98192-5_22
fatcat:npssnl7vgbeuphydjy34psh6ma