New Probabilistic Models for Recommender Systems with Rich Contextual and Content Information
Eliezer Silva · 2017
This project is focused on the design of probabilistic models for recommender systems and collaborative ltering by extending and creating new models to include rich contextual and content information (content, user social network, location, time, user intent, etc), and developing scalable approximate inference algorithms for these models. The working hypothesis is that big data analytics combined with probabilistic modelling, through automatically mining of various data sources and combining di erent latent factors explaining the user interaction with the items, can be used to better infer the user behaviour and generate improved recommendations.