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Tech Talk: Sequential and Session-Based Recommender Systems
Gabriel de Souza P. Moreira, PhD
About 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 can 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.
A special case of sequential-recommendation is the session-based recommendation task, where you have only access to the short sequence of interactions within the current session. This is very common in online services like e-commerce, news and media portals, where the user might be brand new or prefer to browse anonymously (and due to GDPR compliance no cookies are collected). This task is also relevant for scenarios where users’ interests change a lot over time depending on the user context or intent, so leveraging the current session interactions is more promising than old interactions to provide relevant recommendations.
In this talk, it will be presented an overview of the sequential and session-based recommendation tasks, the recent Deep Learning architectures inspired in NLP (RNNs and Transformers) that have been used to tackle those problems and how you can train and build end-to-end recsys pipelines using the tools we have been building for NVIDIA Merlin – an open source platform for large-scale recommender systems – which includes the Transformers4Rec library.
About the Speaker:
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.