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Résumé : While deep learning models have achieved unprecedented success in some challenging perception tasks, probabilistic graphical models are still preferred for many other tasks that require more complex inference mechanisms. To get the best of both worlds, we have been exploring a research direction called Bayesian deep learning (BDL) which seeks to tightly integrate deep learning and probabilistic graphical models within a principled probabilistic framework. In this talk, some BDL models which we have proposed recently for recommendation applications will be presented. Some follow-up work and outstanding research issues for BDL models will also be discussed.