Preferences and Recommendations from Data & AI
Generative models specify procedures allowing us to produce data samples, e.g., images, texts, user preferences, etc. These models are central to unsupervised learning, and...
Data sparsity is one of the biggest challenges in recommender systems. A promising solution to alleviate this problem is to rely on auxiliary sources...
In early August 2018, Aghiles attended the international conference on Uncertainty in Artificial Intelligence (UAI), which took place in Monterey, California. Hereafter, he shares...
