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pro vyhledávání: '"Ebisu, Takuma"'
Autor:
Rahman, Md Mostafizur, Kikuta, Daisuke, Abrol, Satyen, Hirate, Yu, Suzumura, Toyotaro, Loyola, Pablo, Ebisu, Takuma, Kondapaka, Manoj
Publikováno v:
SIGIR 2023
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the hetero
Externí odkaz:
http://arxiv.org/abs/2304.09105
Autor:
Kikuta, Daisuke, Suzumura, Toyotaro, Rahman, Md Mostafizur, Hirate, Yu, Abrol, Satyen, Kondapaka, Manoj, Ebisu, Takuma, Loyola, Pablo
Leveraging graphs on recommender systems has gained popularity with the development of graph representation learning (GRL). In particular, knowledge graph embedding (KGE) and graph neural networks (GNNs) are representative GRL approaches, which have
Externí odkaz:
http://arxiv.org/abs/2205.12102
Autor:
Ebisu, Takuma, Ichise, Ryutaro
Knowledge graphs are useful for many artificial intelligence tasks but often have missing data. Hence, a method for completing knowledge graphs is required. Existing approaches include embedding models, the Path Ranking Algorithm, and rule evaluation
Externí odkaz:
http://arxiv.org/abs/1909.03821
Autor:
Ebisu, Takuma, Ichise, Ryutaro
Knowledge graphs have evolved rapidly in recent years and their usefulness has been demonstrated in many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, many knowledge graph embedding
Externí odkaz:
http://arxiv.org/abs/1904.02856
Autor:
Ebisu, Takuma, Ichise, Ryutaro
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models map entities a
Externí odkaz:
http://arxiv.org/abs/1711.05435
Akademický článek
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Autor:
Ebisu, Takuma, Ichise, Ryutaro
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 32
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models map entities a
Akademický článek
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Autor:
Ebisu, Takuma, Ichise, Ryutaro
Publikováno v:
Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings, Part I; 2016, p300-307, 8p