Výsledky vyhledávání - "Tikk, Domonkos"

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Session-based Recommendations with Recurrent Neural Networks

Autor: Hidasi, Balázs, Karatzoglou, Alexandros, Baltrunas, Linas, Tikk, Domonkos

We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of

Externí odkaz: http://arxiv.org/abs/1511.06939

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General factorization framework for context-aware recommendations

Autor: Hidasi, Balázs, Tikk, Domonkos

Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to

Externí odkaz: http://arxiv.org/abs/1401.4529

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Context-aware recommendations from implicit data via scalable tensor factorization

Autor: Hidasi, Balázs, Tikk, Domonkos

Albeit the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-o

Externí odkaz: http://arxiv.org/abs/1309.7611

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Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback

Autor: Hidasi, Balázs, Tikk, Domonkos

Publikováno v: Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II

Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-

Externí odkaz: http://arxiv.org/abs/1204.1259

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