Add to ORKGIn this project a recommendation system for suggesting movies is implemented, in the field of Collaborative Filtering (CF). The system is created with a Restricted Boltzmann Machine (RBM), which is a two-layer neural network. The main tool used for programming the RBM is the TensorFlow library, imported to Python. The performance of the system is evaluated with Root Mean Squared Error (RMSE) where the error between the observed movie ratings and the predicted ratings is computed. This study shows how different parameters of the RBM, e.g. number of hidden units, mini-batch size, epochs and learning rate, affect the prediction error. The results show that parameter values within a specific range can generate good recommendation with low predi...
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF ...
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of usin...
In this era of big data, the amount of video content has dramatically increased with an exponential ...
In this project a recommendation system for suggesting movies is implemented, in the field of Collab...
One of the most commonly used techniques in the recommendation framework is collaborative filtering ...
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In th...
In this thesis the problem of providing good recommendations to assist users to make the best choice...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
Recommender systems are becoming a large and important market, with commerce moving to the internet ...
Nowadays, the information on the internet presents explosive growth; similar information from the sp...
Collaborative filtering is an effective recommen-dation technique wherein the preference of an indiv...
The massive amount of information available on the World Wide Web has made a requirement for busines...
Recommender systems are programs which attempt to predict items that a user may be interest in. Reco...
Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can u...
In the current era, a rapid increase in data volume produces redundant information on the internet. ...
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF ...
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of usin...
In this era of big data, the amount of video content has dramatically increased with an exponential ...
In this project a recommendation system for suggesting movies is implemented, in the field of Collab...
One of the most commonly used techniques in the recommendation framework is collaborative filtering ...
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In th...
In this thesis the problem of providing good recommendations to assist users to make the best choice...
Recommender systems help people make decisions. They are particularly useful for product recommendat...
Recommender systems are becoming a large and important market, with commerce moving to the internet ...
Nowadays, the information on the internet presents explosive growth; similar information from the sp...
Collaborative filtering is an effective recommen-dation technique wherein the preference of an indiv...
The massive amount of information available on the World Wide Web has made a requirement for busines...
Recommender systems are programs which attempt to predict items that a user may be interest in. Reco...
Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can u...
In the current era, a rapid increase in data volume produces redundant information on the internet. ...
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF ...
This thesis covers the topic of utilizing neural nets for recommending movies. The principle of usin...
In this era of big data, the amount of video content has dramatically increased with an exponential ...