2 citations · 7 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
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…