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    Using Knowledge Graph Embeddings in Embedding Based Recommender Systems

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    Author
    ElKafrawy, Passent cc
    Ragab, Ahmed Hussein
    Subject
    Representation learning , Collaborative filtering , Semantics ,
    Recommender systems
    Electronic commerce ,
    Date
    2023-01
    
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    Abstract
    This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work.
    Department
    Computer Science
    Publisher
    IEEE
    DOI
    https://doi.org/10.1109/ESOLEC54569.2022.10009491
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1109/ESOLEC54569.2022.10009491
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