STUDY: Socially Aware Temporally Casual Decoder Recommender Systems
Authors: name: Eltayeb Ahmed, name: Diana Mincu, name: Lauren Harrell, name: Katherine Heller, name: Subhrajit Roy

With the overwhelming amount of data available both on and offline today,
recommender systems have become much needed to help users find items tailored
to their interests. When social network information exists there are methods
that utilize this information to make better recommendations, however the
methods are often clunky with complex architectures and training procedures.
Furthermore many of the existing methods utilize graph neural networks which
are notoriously difficult to train. To address this, we propose Socially-aware
Temporally caUsal Decoder recommender sYstems (STUDY). STUDY does joint
inference over groups of users who are adjacent in the social network graph
using a single forward pass of a modified transformer decoder network. We test
our method in a school-based educational content setting, using classroom
structure to define social networks. Our method outperforms both social and
sequential methods while maintaining the design simplicity of a single
homogeneous network that models all interactions in the data. We also carry out
ablation studies to understand the drivers of our performance gains and find
that our model depends on leveraging a social network structure that
effectively models the similarities in user behavior.