Dionysius: A Framework for Modeling Hierarchical User Interactions in Recommender Systems
This work addresses the challenge of enhancing personalized recommendations for millions of users on platforms like LinkedIn, though it is incremental as it builds on existing infrastructure with minimal changes.
The authors tackled the problem of incorporating hierarchical user interaction signals into recommender systems while retaining interpretability and leveraging existing infrastructure, resulting in significant improvements in recommendation quality for LinkedIn's job recommendation engine over a year-long deployment.
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.