Social Interactions in Mixed Reality

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.

本研究构建了一种创新的混合现实优先的社交推荐模型，该模型通过混合现实（MR）系统独特收集的特征来促进社交互动，如视线识别、邻近度、噪音水平、拥挤程度以及对话强度。进一步拓展这些模型，融入实时特征以实现及时的通知。我们通过创建用户特征、MR特征和实时特征的新交集，对各种模型进行性能指标测量。我们创建了四种不同特征组合类别的模型，其中将基于用户特征类别的基线模型与基于MR特征、实时特征以及所有特征类别的组合模型进行对比。鉴于数据收集的限制和成本问题，我们在实时、混合现实和组合模型中观察到性能下降。尽管面临这些挑战，我们仍引入了优化措施，以提高所有模型的准确率，使其平均提升超过14个百分点，其中表现最佳的模型准确率提高了24%。