Tonight's Talk

Recommender systems help users to find relevant content, products, media and much more in online services. They also help such services to connect their long-tailed (unpopular) items to the right people, to keep their users engaged and increase conversion.

Traditional recommendation algorithms, e.g. collaborative filtering, usually ignore the temporal dynamics and the sequence of interactions when trying to model user behaviour. But users’ preferences do change over time. Sequential recommendation algorithms are able to capture sequential patterns in users browsing might help to anticipate the next user interests for better recommendation. For example, users getting started into a new hobby like cooking or cycling might explore products for beginners, and move to more advanced products as they progress over time. They might also completely move to another topic of interest, so that recommending items related to their long past preferences would become irrelevant. Read More

Gabriel de Souza P. Moreira, PhD

Gabriel Moreira is a Senior Applied Research Scientist at NVIDIA, leading Merlin team research efforts on recommender systems and also working in the development of Merlin libraries like Transformers4Rec and Merlin Models. He has his PhD degree from Instituto Tecnológico de Aeronáutica (ITA), Brazil with a focus on Deep Learning for RecSys and Session-based recommendation. Before joining NVIDIA, he was Lead Data Scientist at CI&T — a digital transformation consulting company — for 5 years, after working as software engineer for more than a decade. In 2019, he was recognized as a Google Developer Expert (GDE) for Machine Learning.