4 citations · 8 across the 9 of their papers we have counts for
9 papers
GPU accelerated matrix factorization of large scale data using block based approach
Prasad Bhavana, Vineet Padmanabhan
Matrix Factorization (MF) on large scale data takes substantial time on a Central Processing Unit (CPU). While Graphical Processing Unit (GPU)s could expedite the computation of MF…
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data
Sowmini Devi Veeramachaneni, Arun K Pujari, Vineet Padmanabhan +1
Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's…
Decomposing the Deep: Finding Class Specific Filters in Deep CNNs
Akshay Badola, Cherian Roy, Vineet Padmanabhan +1
Interpretability of Deep Neural Networks has become a major area of exploration. Although these networks have achieved state of the art accuracy in many tasks, it is extremely diff…
Inductive Conformal Recommender System
Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan +1
Traditional recommendation algorithms develop techniques that can help people to choose desirable items. However, in many real-world applications, along with a set of recommendatio…
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data
Sowmini Devi Veeramachaneni, Arun K Pujari, Vineet Padmanabhan +1
Recommender systems(RS), especially collaborative filtering(CF) based RS, has been playing an important role in many e-commerce applications. As the information being searched over…
Block based Singular Value Decomposition approach to matrix factorization for recommender systems
Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan
With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems. Producing high quality recommendations with scalability and perform…