Work-related musculoskeletal disorders, often caused by poor lifting posture and unsafe manual handling, continue to pose a significant threat to worker health and safety. This paper presents a health recommender system designed to prevent injury by assessing and correcting posture for lifting techniques. Leveraging monocular video input, our method estimates key ergonomic parameters to compute the Lifting Index based on the Revised NIOSH Lifting Equation. When the computed Lifting Index exceeds a predefined safety threshold, the system automatically generates graphical and textual recommendations to guide the worker towards safer postural strategies. This safety-aware recommender system provides interpretable and actionable feedback without requiring wearable sensors or multi-camera setups, making it suitable for deployment in real-world workplace environments. By integrating ergonomics with recommender system design, we contribute to a new class of context-aware, safety-oriented recommendation technologies tailored for occupational health.

As intelligent interactive systems, recommender systems focus on determining predictions thatfit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems from a human decision making perspective. Methodologies for evaluating these aspects are also within the scope of the workshop.