Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information

Amar Budhiraja, Gaurush Hiranandani, Darshak Chhatbar, Aditya Sinha, Navya Yarrabelly, Ayush Choure, Sanmi Koyejo, Prateek Jain

Abstract:

In this paper, we study the problem of recommendation system where the users/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/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.