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Found 8 papers, 4 papers with code
Reliability quality measures for recommender systems
Both quality measures are based on the hypothesis that the more suitable a reliability measure is, the better accuracy results it will provide when applied.
Deep Neural Aggregation for Recommending Items to Group of Users
One key tool for the digital society is Recommender Systems, intelligent systems that learn from our past actions to propose new ones that align with our interests.
Neural Group Recommendation Based on a Probabilistic Semantic Aggregation
Recommendation to groups of users is a challenging subfield of recommendation systems.
Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks
Research and education in machine learning needs diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks.
Deep Variational Models for Collaborative Filtering-based Recommender Systems
On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems.
Deep Learning feature selection to unhide demographic recommender systems factors
This paper provides a deep learning-based method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems.
DeepFair: Deep Learning for Improving Fairness in Recommender Systems
The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations.
Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
Beyond accuracy, quality measures are gaining importance in modern recommender systems, with reliability being one of the most important indicators in the context of collaborative filtering.
