This thesis investigates how recommendation systems has been used and can be used with the help of different machine learning algorithms. Algorithms used and presented are decision tree, random forest and singular-value decomposition(SVD). Together with Tingstad, we have tried to implement the SVD function on their recommendation engine in order to enhance the recommendation given. A trivial presentation on how the algorithms work. General information about machine learning and how we tried to implement it with Tingstad’s data. Implementations with Netflix’s and Movielens open-source dataset was done, estimated with RMSE and MAE
Personalized recommendations are of key importance when it comes to increasing business value and sa...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
This thesis investigates how recommendation systems has been used and can be used with the help of d...
Recommender systems apply machine learning and data mining techniques for filtering unseen informati...
The aim of this project is to develop an approach using machine learning and matrix factorization to...
Recommendation systems are subdivision of Refine Data that request to anticipate ranking or liking a...
Abstract—PolyFlix is a movie recommendation system targeted for the Netflix prize competition. PolyF...
Nowadays, recommendation systems are used successfully to provide items (example: movies, music, boo...
A recommendation system is a system that provides online users with recommendations for particular r...
This timely book presents Applications in Recommender Systems which are making recommendations using...
In this paper, I applied several machine learning techniques, including Latent Dirichlet allocation ...
Automated systems which can accurately surface relevant content for a given query have become an ind...
This thesis explores how different recommendation models based on machine learning can be implemente...
Document ranking systems and recommender systems are two of the most used applications on the intern...
Personalized recommendations are of key importance when it comes to increasing business value and sa...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
This thesis investigates how recommendation systems has been used and can be used with the help of d...
Recommender systems apply machine learning and data mining techniques for filtering unseen informati...
The aim of this project is to develop an approach using machine learning and matrix factorization to...
Recommendation systems are subdivision of Refine Data that request to anticipate ranking or liking a...
Abstract—PolyFlix is a movie recommendation system targeted for the Netflix prize competition. PolyF...
Nowadays, recommendation systems are used successfully to provide items (example: movies, music, boo...
A recommendation system is a system that provides online users with recommendations for particular r...
This timely book presents Applications in Recommender Systems which are making recommendations using...
In this paper, I applied several machine learning techniques, including Latent Dirichlet allocation ...
Automated systems which can accurately surface relevant content for a given query have become an ind...
This thesis explores how different recommendation models based on machine learning can be implemente...
Document ranking systems and recommender systems are two of the most used applications on the intern...
Personalized recommendations are of key importance when it comes to increasing business value and sa...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...
Due to modern information and communication technologies (ICT), it is increasingly easier to exchang...