2 citations · 7 across the 14 of their papers we have counts for
15 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…
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…