2 citations · 5 across the 11 of their papers we have counts for
12 papers
Geometric Preference Elicitation for Minimax Regret Optimization in Uncertainty Matroids
Aditya Sai Ellendula, Arun K Pujari, Vikas Kumar +1
This paper presents an efficient preference elicitation framework for uncertain matroid optimization, where precise weight information is unavailable, but insights into possible we…
Gradient Based Hybridization of PSO
Arun K Pujari, Sowmini Devi Veeramachaneni
Particle Swarm Optimization (PSO) has emerged as a powerful metaheuristic global optimization approach over the past three decades. Its appeal lies in its ability to tackle complex…
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
Adamya Shyam, Vikas Kumar, Venkateswara Rao Kagita +1
Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more info…
Conformal Group Recommender System
Venkateswara Rao Kagita, Anshuman Singh, Vikas Kumar +3
Group recommender systems (GRS) are critical in discovering relevant items from a near-infinite inventory based on group preferences rather than individual preferences, like recomm…
Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization
Shamal Shaikh, Venkateswara Rao Kagita, Vikas Kumar +1
Collaborative filtering (CF) has become a popular method for developing recommender systems (RSs) where ratings of a user for new items are predicted based on her past preferences…
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…