Recent popularity surrounds large AI language models due to their impres...
The successful integration of graph neural networks into recommender sys...
The success of graph neural network-based models (GNNs) has significantl...
Information Retrieval (IR) and Recommender Systems (RS) tasks are moving...
Current AI regulations require discarding sensitive features (e.g., gend...
The increasing application of Artificial Intelligence and Machine Learni...
Research on recommender systems algorithms, like other areas of applied
...
Explainable Recommendation has attracted a lot of attention due to a ren...
Deep Learning and factorization-based collaborative filtering recommenda...
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to m...
Collaborative filtering models based on matrix factorization and learned...
Recommender Systems have shown to be an effective way to alleviate the
o...
Recommender systems have shown to be a successful representative of how ...
Recommender systems (RSs) have attained exceptional performance in learn...
Visual-based recommender systems (VRSs) enhance recommendation performan...
Recommendation services are extensively adopted in several user-centered...
In Machine Learning scenarios, privacy is a crucial concern when models ...
Model-based approaches to recommendation can recommend items with a very...
Hyper-parameters tuning is a crucial task to make a model perform at its...
With the wealth of information produced by social networks, smartphones,...
Fairness in recommender systems has been considered with respect to sens...
Similarity measures play a fundamental role in memory-based nearest neig...
Items popularity is a strong signal in recommendation algorithms. It aff...