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Found 8 papers, 1 papers with code
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
We consider two prominent CF techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks.
Conformal Group Recommender System
The traditional models of group recommendation are designed to act like a black box with a strict focus on improving recommendation accuracy, and most often, they place the onus on the users to interpret recommendations.
Cross-domain Recommender Systems via Multimodal Domain Adaptation
Several approaches in the literature have been proposed to tackle the problem of data sparsity, among which cross-domain collaborative filtering (CDCF) has gained significant attention in the recent past.
Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization
We exploit the inherent characteristics of CF algorithms to assess the confidence level of individual ratings and propose a semi-supervised approach for rating augmentation based on self-training.
On Robustness of Finetuned Transformer-based NLP Models
Further, how robust are these models to perturbations in input text?
Inductive Conformal Recommender System
The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value.
Committee Selection with Attribute Level Preferences
We describe a greedy approach for attribute aggregation that satisfies the first three properties, but not the fourth, i. e., compound justified representation, which we prove to be NP-complete.
Group Preserving Label Embedding for Multi-Label Classification
Multi-label learning is concerned with the classification of data with multiple class labels.
