Researcher
Current- Work on Embedding representations for multi-target Cross-Domain Recommendation.- Concentrate on multi-target CDR models applied to multiple types of items with an overlap on the set of users, and address through embedding-based transfer approaches, from classic embeddings creation based on matrix factorizations to the most modern approaches using deep learning and transfer learning.- Overview of single-domain recommender systems. Special attention to CF and DL approaches.- Adapt to work in a multi-target Cross-Domain Recommendation context.- Comparison analysis of the different approaches and conduct empirical study on various techniques, like transfer learning (BERT4Rec, ShopperBERT, etc.), and multi-task learning.