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.

翻译：我们构建了一种创新的以混合现实为先的社交推荐模型，利用混合现实（MR）系统独有的特征（如视线识别、距离、噪音水平、拥挤程度以及对话强度）来促进社交互动。我们进一步扩展这些模型，融入实时性特征以发送及时通知。通过创建用户特征、MR特征和实时性特征的新交集，我们测量了不同模型的性能指标。我们基于特征类别的不同组合训练了四种模型类型，将基于用户特征类别训练的基线模型与基于MR特征、实时性特征以及所有特征类别组合训练的模型进行了对比。由于数据收集成本和限制，我们观察到实时性模型、混合现实模型及组合模型的性能有所下降。尽管面临这些挑战，我们引入了优化方法，将所有模型的准确率提高了超过14个百分点，其中表现最佳的模型实现了24%的准确率提升。