Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.19095 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929331564969984 |
|---|---|
| author | Srivastava, Sparsh Arora, Rohan |
| author_facet | Srivastava, Sparsh Arora, Rohan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19095 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Catalyzing Social Interactions in Mixed Reality using ML Recommendation Systems Srivastava, Sparsh Arora, Rohan Human-Computer Interaction Information Retrieval Machine Learning Social and Information Networks 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. |
| title | Catalyzing Social Interactions in Mixed Reality using ML Recommendation Systems |
| topic | Human-Computer Interaction Information Retrieval Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2404.19095 |