  In this paper, we study the problem of recommendation system where the users
and items to be recommended are rich data structures with multiple entity types
and with multiple sources of side-information in the form of graphs. We provide
a general formulation for the problem that captures the complexities of modern
real-world recommendations and generalizes many existing formulations. In our
formulation, each user/document that requires a recommendation and each item or
tag that is to be recommended, both are modeled by a set of static entities and
a dynamic component. The relationships between entities are captured by several
weighted bipartite graphs. To effectively exploit these complex interactions
and learn the recommendation model, we propose MEDRES- a multiple graph-CNN
based novel deep-learning architecture. MEDRES uses AL-GCN, a novel graph
convolution network block, that harnesses strong representative features from
the underlying graphs. Moreover, in order to capture highly heterogeneous
engagement of different users with the system and constraints on the number of
items to be recommended, we propose a novel ranking metric pAp@k along with a
method to optimize the metric directly. We demonstrate effectiveness of our
method on two benchmarks: a) citation data, b) Flickr data. In addition, we
present two real-world case studies of our formulation and the MEDRES
architecture. We show how our technique can be used to naturally model the
message recommendation problem and the teams recommendation problem in the
Microsoft Teams (MSTeams) product and demonstrate that it is 5-6% points more
accurate than the production-grade models.
