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A New Perspective in Health Recommendations: Integration of Human Pose Estimation

Published: 08 October 2024 Publication History

Abstract

In recent years, there has been a growing interest in multimodal and multi-source data due to their ability to introduce heterogeneous information. Studies have demonstrated that combining such information enhances the performance of Recommender Systems across various scenarios. In the context of Health Recommendation Systems (HRS), different types of data are utilized, primarily focusing on patient-based information, but data from Pose Estimations (PE) are not incorporated.
The objective of my Ph.D. is to investigate methods to design and develop HRS that treat the PE as one of the input sources, taking into account aspects such as privacy concerns and balancing the trade-off between system quality and responsiveness. By leveraging the combination of diverse information sources, I intend to create a new model in the area of HRS capable of providing more precise and explainable recommendations.

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      cover image ACM Conferences
      RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
      October 2024
      1438 pages
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      Published: 08 October 2024

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      Author Tags

      1. Explanability
      2. Health Recommender Systems
      3. Pose Estimation
      4. Privacy

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