Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions
(2017)
Presentation / Conference
Pozo, M., Chiky, R., Meziane, F., & Metais, E. (2017, November). Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions. Presented at 33ème conférence sur la Gestion de Données — Principes, Technologies et Applications, Nancy, France
The cold-start is the situation in which the recommender system has no or not enough information about the (new) users/items i.e. their ratings/feedback; hence, the recommendations are not well performed. This issue is commonly encountered in techniq... Read More about Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions.