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Found 9 papers, 0 papers with code
Gradient Based Hybridization of PSO
These strategies aim to enhance the efficiency and effectiveness of PSO, ultimately improving its ability to navigate intricate optimization landscapes.
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.
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.
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data
In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique.
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.
A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data
By making use of hinge loss function we transfer the learnt codebook of the source domain to target.
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.
