4 citations · 6 across the 10 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…