Recommendation algorithms optimization
Optimalizace doporučovacích algoritmů
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České vysoké učení technické v Praze
Czech Technical University in Prague
Czech Technical University in Prague
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V poslednich letech se vyvinulo velké množstvi rozličných doporučovacich al-goritmů. Jednu věc maji ale všechny společnou. Jejich hyper-parametry se musi pečlivě zvolit, aby dosahovaly dobrých výsledků. Tato práce se zabývá výběrem takových algoritmů a navrhnutim optimal-izačni procedury, která bude schopná nalézt vhodné hyper-parametry těchto algoritmů. Výsledky jsou pak ověřeny na reálných datasetech.
Various recommendation algorithms have been proposed in recent years. However, each of them has one thing in common. It is essential to tune their hyper-parameters in order to achieve good results. This work has focused on selecting modern and scalable algorithms. The aim has been to design and implement an optimization procedure capable of fine-tuning their hyper-parameters and evaluate the results on real-world datasets.
Various recommendation algorithms have been proposed in recent years. However, each of them has one thing in common. It is essential to tune their hyper-parameters in order to achieve good results. This work has focused on selecting modern and scalable algorithms. The aim has been to design and implement an optimization procedure capable of fine-tuning their hyper-parameters and evaluate the results on real-world datasets.