Nitin Pradeep Kumar - Academia.edu (original) (raw)

Nitin Pradeep Kumar

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Papers by Nitin Pradeep Kumar

Research paper thumbnail of International Conference on Knowledge Based and Intelligent Information and Engineering Systems Hybrid User-Item Based Collaborative Filtering

Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative ... more Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International.

Research paper thumbnail of Hybrid User-Item Based Collaborative Filtering

Procedia Computer Science, 2015

Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative ... more Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm.

Research paper thumbnail of Parallelizing TUNAMI-N1 Using GPGPU

2011 IEEE International Conference on High Performance Computing and Communications, 2011

Abstract We present a high performance tsunami-prediction system using General Purpose Graphics P... more Abstract We present a high performance tsunami-prediction system using General Purpose Graphics Processing Units (GPGPU). It is based on TUNAMI-N1, a Numerical Analysis Model for Investigation of near-field tsunamis. It uses linear shallow water wave equations, ...

Research paper thumbnail of International Conference on Knowledge Based and Intelligent Information and Engineering Systems Hybrid User-Item Based Collaborative Filtering

Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative ... more Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of KES International.

Research paper thumbnail of Hybrid User-Item Based Collaborative Filtering

Procedia Computer Science, 2015

Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative ... more Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm.

Research paper thumbnail of Parallelizing TUNAMI-N1 Using GPGPU

2011 IEEE International Conference on High Performance Computing and Communications, 2011

Abstract We present a high performance tsunami-prediction system using General Purpose Graphics P... more Abstract We present a high performance tsunami-prediction system using General Purpose Graphics Processing Units (GPGPU). It is based on TUNAMI-N1, a Numerical Analysis Model for Investigation of near-field tsunamis. It uses linear shallow water wave equations, ...

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