4 citations · 6 across the 11 of their papers we have counts for
13 papers
GrIT: Group Informed Transformer for Sequential Recommendation
Adamya Shyam, Venkateswara Rao Kagita, Bharti Rana +1
Sequential recommender systems aim to predict a user's future interests by extracting temporal patterns from their behavioral history. Existing approaches typically employ transfor…
DReX: An Explainable Deep Learning-based Multimodal Recommendation Framework
Adamya Shyam, Venkateswara Rao Kagita, Bharti Rana +1
Multimodal recommender systems leverage diverse data sources, such as user interactions, content features, and contextual information, to address challenges like cold-start and dat…
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…
Social Welfare Maximization in Approval-Based Committee Voting under Uncertainty
Haris Aziz, Yuhang Guo, Venkateswara Rao Kagita +2
Approval voting is widely used for making multi-winner voting decisions. The canonical rule (also called Approval Voting) used in the setting aims to maximize social welfare by sel…
Approval-Based Committee Voting under Uncertainty
Hariz Aziz, Venkateswara Rao Kagita, Baharak Rastegari +1
We study approval-based committee voting in which a target number of candidates are selected based on voters' approval preferences over candidates. In contrast to most of the work,…
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…
Cross-domain Recommender Systems via Multimodal Domain Adaptation
Adamya Shyam, Ramya Kamani, Venkateswara Rao Kagita +1
Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of i…
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…
On Robustness of Finetuned Transformer-based NLP Models
Pavan Kalyan Reddy Neerudu, Subba Reddy Oota, Mounika Marreddy +2
Transformer-based pretrained models like BERT, GPT-2 and T5 have been finetuned for a large number of natural language processing (NLP) tasks, and have been shown to be very effect…
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…
Committee Selection with Attribute Level Preferences
Venkateswara Rao Kagita, Arun K Pujari, Vineet Padmanabhan +1
We consider the problem of committee selection from a fixed set of candidates where each candidate has multiple quantifiable attributes. To select the best possible committee, inst…
Group Preserving Label Embedding for Multi-Label Classification
Vikas Kumar, Arun K Pujari, Vineet Padmanabhan +1
Multi-label learning is concerned with the classification of data with multiple class labels. This is in contrast to the traditional classification problem where every data instanc…