Catalyzing Social Interactions in Mixed Reality using ML Recommendation Systems

TL;DR

This paper introduces a mixed reality social recommendation system leveraging MR-specific features and right-time notifications to enhance social interactions, with optimized models showing significant accuracy improvements despite data limitations.

Contribution

It presents a novel MR-first social recommendation model using unique MR features and right-time data, with optimizations that improve accuracy by over 14 percentage points.

Findings

Models trained on combined features outperform baseline.

Performance degradation observed due to data collection limitations.

Optimizations lead to over 14% accuracy improvement.

Abstract

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.