  We address the following problem: How do we incorporate user item interaction
signals as part of the relevance model in a large-scale personalized
recommendation system such that, (1) the ability to interpret the model and
explain recommendations is retained, and (2) the existing infrastructure
designed for the (user profile) content-based model can be leveraged? We
propose Dionysius, a hierarchical graphical model based framework and system
for incorporating user interactions into recommender systems, with minimal
change to the underlying infrastructure. We learn a hidden fields vector for
each user by considering the hierarchy of interaction signals, and replace the
user profile-based vector with this learned vector, thereby not expanding the
feature space at all. Thus, our framework allows the use of existing
recommendation infrastructure that supports content based features. We
implemented and deployed this system as part of the recommendation platform at
LinkedIn for more than one year. We validated the efficacy of our approach
through extensive offline experiments with different model choices, as well as
online A/B testing experiments. Our deployment of this system as part of the
job recommendation engine resulted in significant improvement in the quality of
retrieved results, thereby generating improved user experience and positive
impact for millions of users.
