  We create an innovative mixed reality-first social recommendation model,
utilizing features uniquely collected through mixed reality (MR) systems to
promote social interaction, such as gaze recognition, proximity, noise level,
congestion level, and conversational intensity. We further extend these models
to include right-time features to deliver timely notifications. We measure
performance metrics across various models by creating a new intersection of
user features, MR features, and right-time features. We create four model types
trained on different combinations of the feature classes, where we compare the
baseline model trained on the class of user features against the models trained
on MR features, right-time features, and a combination of all of the feature
classes. Due to limitations in data collection and cost, we observe performance
degradation in the right-time, mixed reality, and combination models. Despite
these challenges, we introduce optimizations to improve accuracy across all
models by over 14 percentage points, where the best performing model achieved
24% greater accuracy.
