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Katrien Verbert
Francisco Gutiérrez
[Augment/HCI - KU Leuven]
Katrien.verbert@kuleuven.be
@katrien_v | Twitter
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Personalized food recommendations: combining recommendation, visualization and augmented reality techniques for healthier food decision-making
This document discusses using augmented reality and recommendation techniques to promote healthier food choices. It proposes combining recommendation systems, visualization of nutritional information, and augmented reality to support decision making at grocery stores. A user study tested an augmented reality application called PHARA that provided nutritional information and recommendations for food products on a HoloLens and smartphone. Results found the application led users to select healthier options over time and that interface layouts tailored for each device led to better performance and user experience. The work aims to eventually influence long-term healthy shopping behavior through such in-situ recommendations and motivational design.
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Personalized food recommendations: combining recommendation, visualization and augmented reality techniques for healthier food decision-making
- 1. Personalized food recommendations:combining recommendation, visualization and augmented reality techniques for healthier food decision-making 1 https://augment.cs.kuleuven.be/ Katrien Verbert, Francisco Gutiérrez Augment/HCI - @katrien_v
- 2. recommender systems –visualization – intelligent user interfaces Learning analytics Media consumption Research Information Systems Food & health Augment prof. Katrien Verbert ARIA prof. Adalberto Simeone Computer Graphics prof. Phil Dutré Language Intelligence & Information Retrieval prof. Sien Moens Motivational design Prof. Vero Vanden Abeele Accessibility Prof. Kathrin Gerling Human-computer interaction group
- 3. Augment/HCI team Robin DeCroon Postdoc researcher Katrien Verbert Associate Professor Francisco Gutiérrez Postdoc researcher Tom Broos PhD researcher Martijn Millecamp PhD researcher Nyi Nyi Htun Postdoc researcher Houda Lamqaddam PhD researcher Jeroen Ooge PhD researcher Oscar Alvarado PhD researcher http://augment.cs.kuleuven.be/ Diego Rojo Carcia PhD researcher Leen Van Houdt PhD researcher
- 4. Collaborative filtering –Content-based filtering – Knowledge-based filtering - Hybrid Recommendation techniques
- 5. Two ways torecommend healthier food choices 5 Recipes recommend recipes for healthier meal (or meal plans / sets of recipes) Food items suggest healthier food items themselves
- 6. 6Trattner, C., &Elsweiler, D. (2017). Food recommender systems: important contributions, challenges and future research directions. arXiv preprint arXiv:1711.02760. Survey of 18 food recommender systems Wide variety of CB, KB, CF, hybrid techniques Datasets: Allrecipes, Yummly, Kochbar ….
- 7. Challenge 1: userstend to like unhealthy recipes 7 Trattner, C., & Elsweiler, D. (2017). Food recommender systems: important contributions, challenges and future research directions. arXiv preprint arXiv:1711.02760.
- 8. Our approach: takeinto account nutritional scores 8 Gutiérrez, F., Htun, N. N., Charleer, S., De Croon, R., & Verbert, K. (2019, January). Designing Augmented Reality Applications for Personal Health Decision-Making. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
- 9. Challenge 2: motivatingusers Users need to be motivated to select healthier products / recipes Our approach: visualize nutritional facts and impact on weight using a predictive model 9
- 10. Challenge 3: needfor in-situ recommendations Recommendations and visualizations need to be delivered at critical moment of decision: when users hold the food product in their hands Our approach: using augmented reality techniques to support decision-making in grocery stores 10
- 11. 11 Food labels are consideredan essential element in strategies against unhealthy diets and obesity. (Cecchini and Warin, 2016)
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- 13. 13 PHARA: Personal HealthAugmented Reality assistant
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- 15. 15 Iterative design Gutiérrez, F.,Cardoso, B. D. L. R. P., & Verbert, K. (2017, August). PHARA: a Personal Health Augmented Reality Assistant to Support Decision-Making at Grocery Stores. In Healthrecsys@ recsys (pp. 10-13).
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- 17. 17 AR components Users' DecisionBehavior in Recommender Interfaces: Impact of Layout Design (Chen and Tsoi, 2011)
- 18. 18 a) MS HoLolens(HMD) c) iPhone 6S (Smartphone) b) NSW Joycon (controller) Devices
- 19. List Layout Stack Layout 19 ARcomponents Smartphone HoloLens
- 20. 20 AR components List Layout ListLayout Smartphone HoloLens
- 21. 21 AR components List Layout GridLayout Smartphone HoloLens
- 22. 22 AR components List Layout PieLayout Smartphone HoloLens
- 23. ● Within Subjects ●n = 28 (1F, 27M) Ages from 22 to 38 (M = 25.81, SD = 4.57) ● Number of conditions: 2 (HoloLens, Smartphone) ● Task 1: - Select two products that you would like to have for dinner... - Select two similar products... - Select and replace with two alternatives... - Reflection... ● Task 2: - Imagine that you have some friends coming over... select two products… and put them in the basket. ● Post-Questionnaires ○ TAM (Technology Acceptance) ○ NASA-TLX (Task Load Index) 23 User study
- 24. 24 Results food choices overtime Choice1 Choice 2 Choice 3 Choice 4 Choice 1 Choice 2 Choice 3 Choice 4
- 25. 25 Results Gutiérrez Hernández, Francisco;Htun, Nyi Nyi; Charleer, Sven; De Croon, Robin; Verbert, Katrien; 2019. Designing augmented reality applications for personal health decision-making. Proceedings of the 2019 52nd Hawaii International Conference on System Sciences; 2019; pp. 1738 - 1747
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- 28. 28 Conclusions ● PHARA allowsusers to make informed decisions, and resulted in selecting healthier food products. ● Stack layout performs better with HMD devices with a limited field of view, like the HoloLens, at the cost of some affordances. ● The grid and pie layouts performed better in handheld devices, allowing to explore with more confidence, enjoyability and less effort.
- 29. 29 Future work ● Long-termgoal: influence healthy grocery store shopping and in-restaurant purchase behavior. ● Evaluate the potential of our approach in real-life settings. ● Integrate other motivational design techniques. ● Wine decision-support scenario coming up.
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- 31. AHMOSE: Augmented byhuman model selection 3 1 https://ahmose.ml/
- 32. 32 Thank you! Katrien Verbert FranciscoGutiérrez [Augment/HCI - KU Leuven] Katrien.verbert@kuleuven.be @katrien_v | Twitter https://augment.cs.kuleuven.be/