In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results. Nick Pentreath explores the latest research advances in this domain, as well as practical applications.
Nick offers an overview of RNNs, covering common architectures and applications, before diving deeper into RNNs for session-based recommendations. Nick pays particular attention to the challenges inherent in common personalization tasks and the specific adjustments to models and optimization techniques required for success.
Nick Pentreath is a principal engineer at the Center for Open Source Data & AI Technologies (CODAIT) at IBM, where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations, and was at Goldman Sachs, Cognitive Match, and Mxit. He’s a committer and PMC member of the Apache Spark project and author of Machine Learning with Spark. Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.
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