Explainable AI; Interactive; Nutrition virtual coach; Recommender systems; Argumentation systems; Data driven; Healthy lifestyles; Human machine interaction; Mechanistics; User engagement; Users' acceptance; Artificial Intelligence
Résumé :
[en] The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human-machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Buzcu, Berk; Computer Science, Özyeğin University, Istanbul, Turkey ; University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), Sierre, Switzerland
Tessa, Melissa; Computer Science, High National School of Computer Science ESI ex-INI, Algiers, Algeria
TCHAPPI HAMAN, Igor ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
NAJJAR, Amro ; University of Luxembourg ; Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
HULSTIJN, Joris ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Calvaresi, Davide; University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis), Sierre, Switzerland
Aydoğan, Reyhan; Computer Science, Özyeğin University, Istanbul, Turkey ; Interactive Intelligence, Delft University of Technology, Delft, The Netherlands ; University of Alcala, Alcala de Henares, Spain
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Towards interactive explanation-based nutrition virtual coaching systems.
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu CHIST-ERA Fonds National de la Recherche Luxembourg Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung University of Applied Sciences and Arts Western Switzerland
Subventionnement (détails) :
This work has been supported by the CHIST-ERA grant CHIST-ERA-19-XAI-005, and by the Swiss National Science Foundation (G.A. 20CH21_195530), the Italian Ministry for Universities and Research, the Luxembourg National Research Fund (G.A. INTER/CHIST/19/14589586), the Scientific and Research Council of Turkey (TÜBİTAK, G.A. 120N680).Open access funding provided by University of Applied Sciences and Arts Western Switzerland (HES-SO). This work has been supported by the CHIST-ERA grant CHIST-ERA-19-XAI-005, and by the Swiss National Science Foundation (G.A. 20CH21_195530), the Italian Ministry for Universities and Research, the Luxembourg National Research Fund (G.A. INTER/CHIST/19/14589586), the Scientific and Research Council of Turkey (TÜBİTAK, G.A. 120N680).
. Sigir ’17: Proceedings of the 40th international ACM sigir conference on research and development in information retrieval. Association for Computing Machinery.
. Yemek tarifleri. https://www.diyetkolik.com/yemek-tarifleri/. Accessed 01 January 2022.
Ancona, M., Ceolini, E., & Öztireli, A. C. (2017). A unified view of gradient-based attribution methods for deep neural networks. In CoRR.
Anjomshoae, S., Najjar, A., Calvaresi, D., & Framling, K. (2019). Explainable agents and robots: Results from a systematic literature review. In AAMAS, Montreal, Canada, May 13–17 (pp. 1078–1088).
Ayub, M., Ghazanfar, M. A., Maqsood, M., & Saleem, A. (2018). A Jaccard base similarity measure to improve performance of CF based recommender systems (pp 1–6).
Buzcu, B., Varadhajaran, V., Tchappi, I., et al. (2022). Explanation-based negotiation protocol for nutrition virtual coaching (pp. 20–36). Springer.
Calvaresi, D., Calbimonte, J. P., Siboni, E., Eggenschwiler, S., Manzo, G., Hilfiker, R., & Schumacher, M. (2021). Erebots: Privacy-compliant agent-based platform for multi-scenario personalized health-assistant chatbots. Electronics, 10, 666. DOI: 10.3390/electronics10060666
Calvaresi, D., Carli, R., Piguet, J. G., Contreras, V. H., Luzzani, G., Najjar, A., & Schumacher, M. (2022) Ethical and legal considerations for nutrition virtual coaches. In AI and ethics (pp. 1–28).
Cemiloglu, D., Catania, M., & Ali, R. (2021). Explainable persuasion in interactive design. In 2021 IEEE 29th international requirements engineering conference workshops (REW) (pp. 377–382).
Chen, M., Jia, X., Gorbonos, E., et al. (2020). Eating healthier: Exploring nutrition information for healthier recipe recommendation. Information Processing & Management, 57(6), 102051. DOI: 10.1016/j.ipm.2019.05.012
Chi, Y. L., Chen, T. Y., & Tsai, W. T. (2015). A chronic disease dietary consultation system using owl-based ontologies and semantic rules. Journal of Biomedical Informatics, 53, 208–219. DOI: 10.1016/j.jbi.2014.11.001
Corrado, S., Luzzani, G., Trevisan, M., & Lamastra, L. (2019). Contribution of different life cycle stages to the greenhouse gas emissions associated with three balanced dietary patterns. Science of the Total Environment, 660, 622–630. DOI: 10.1016/j.scitotenv.2018.12.267
El-Dosuky, M., Rashad, M. Z., Hamza, T., & El-Bassiouny, A. H. (2012). Food recommendation using ontology and heuristics. In Advanced machine learning technologies and applications: First international conference, AMLTA 2012, Cairo, Egypt, December 8–10, 2012. Proceedings 1 (pp. 423–429). Springer.
Fanda, L., Cid, Y. D., Matusz, P. J., & Calvaresi, D. (2021). To pay or not to pay attention: Classifying and interpreting visual selective attention frequency features. In International workshop on explainable, transparent autonomous agents and multi-agent systems (pp. 3–17). Springer.
Forbes, P., & Zhu, M. (2011). Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation. In Proceedings of the fifth ACM conference on recommender systems (pp. 261–264).
Freyne, J., & Berkovsky, S. (2010). Recommending food: Reasoning on recipes and ingredients (pp. 381–386).
Ge, M., Ricci, F., & Massimo, D. (2015). Health-aware food recommender system. In Proceedings of the 9th ACM conference on recommender systems (RecSys ’15, pp. 333–334). Association for Computing Machinery.
Gibney, M. J., Forde, C. G., Mullally, D., & Gibney, E. R. (2017). Ultra-processed foods in human health: A critical appraisal. The American Journal of Clinical nutrition, 106(3), 717–724. DOI: 10.3945/ajcn.117.160440
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5), 1–42. DOI: 10.1145/3236009
Gunning, D., & Aha, D. (2019). Darpa’s explainable artificial intelligence (xai) program. AI Magazine, 40(2), 44–58. DOI: 10.1609/aimag.v40i2.2850
Hammond, K. J. (1986). Chef: A model of case-based planning. In AAAI.
Harris, J. A., & Benedict, F. G. (1918). A biometric study of human basal metabolism. Proceedings of the National Academy of Sciences, 4(12), 370–373. DOI: 10.1073/pnas.4.12.370
Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for explainable AI: Challenges and prospects. arXiv:1812.04608 [csAI]
Hulstijn, J., Tchappi, I., Najjar, A., Aydogan, R. (2023). (2023) Metrics for evaluating explainable recommender systems. In AAMAS, EXTRAAMAS 2023. Springer.
Ishizaka, A., & Siraj, S. (2018). Are multi-criteria decision-making tools useful? an experimental comparative study of three methods. European Journal of Operational Research, 264(2), 462–471. DOI: 10.1016/j.ejor.2017.05.041
Jaccard, P. (1912). The distribution of the flora in the alpine zone 1. New Phytologist, 11(2), 37–50. DOI: 10.1111/j.1469-8137.1912.tb05611.x
Jannach, D., Pu, P., Ricci, F., & Zanker, M. (2021). Recommender systems: Past, present, future. AI Magazine, 42, 3–6. DOI: 10.1609/aimag.v42i3.18139
Lawo, D., Neifer, T., Esau, M., Stevens, G. (2021) Buying the ‘right’ thing: Designing food recommender systems with critical consumers. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery.
Lazar, J., Feng, J.,; Hochheiser, H. (2017). Research methods in human–computer interaction
Millecamp, M., Htun, N. N., Conati, C., & Verbert, K. (2019). To explain or not to explain: The effects of personal characteristics when explaining music recommendations. In Proceedings of the 24th international conference on intelligent user interfaces (pp. 397–407).
Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. DOI: 10.1016/j.artint.2018.07.007
Mokdara, T., Pusawiro, P., Harnsomburana, J. (2018), Personalized food recommendation using deep neural network. In 2018 Seventh ICT international student project conference (ICT-ISPC) (pp. 1–4). IEEE.
Mualla, Y., Tchappi, I., Kampik, T., Najjar, A., Calvaresi, D., Abbas-Turki, A., & Nicolle, C. (2022). The quest of parsimonious Xai: A human-agent architecture for explanation formulation. Artificial Intelligence, 302, 103573. DOI: 10.1016/j.artint.2021.103573
Padhiar, I., Seneviratne, O., Chari, S., Gruen, D., & McGuinness, D. L. (2021). Semantic modeling for food recommendation explanations (pp. 13–19).
Saarela, M., & Jauhiainen, S. (2021). Comparison of feature importance measures as explanations for classification models. SN Applied Sciences, 3, 1–12. DOI: 10.1007/s42452-021-04148-9
Samih, A., Adadi, A., & Berrada, M. (2019). Towards a knowledge based explainable recommender systems. In Proceedings of the 4th international conference on big data and internet of things (BDIoT’19). Association for Computing Machinery.
Shimazu, H. (2001). Expertclerk: Navigating shoppers’ buying process with the combination of asking and proposing. In Proceedings of the 17th international joint conference on artificial intelligence (IJCAI’01, Vol. 2, pp. 1443–1448). Morgan Kaufmann Publishers Inc.
Sovrano, F., & Vitali, F. (2022). Generating user-centred explanations via illocutionary question answering: From philosophy to interfaces. ACM Transactions on Interactive Intelligent Systems, 12(4), 1–32. DOI: 10.1145/3519265
Teng, C. Y., Lin, Y. R., & Adamic, L. A. (2012a). Recipe recommendation using ingredient networks. In Proceedings of the 4th annual ACM web science conference (WebSci ’12, pp. 298–307)
Teng, C. Y., Lin, Y. R., & Adamic, L. A. (2012b). Recipe recommendation using ingredient networks. In Proceedings of the 4th annual ACM web science conference (pp. 298–307).
Tintarev, N., & Masthoff, J. (2015). Explaining recommendations: Design and evaluation. Recommender systems handbook (pp. 353–382).
Toledo, R. Y., Alzahrani, A. A., & Martinez, L. (2019). A food recommender system considering nutritional information and user preferences. IEEE Access, 7, 96695–96711. DOI: 10.1109/ACCESS.2019.2929413
Trang Tran, T. N., Atas, M., Felfernig, A., & Stettinger, M. (2018). An overview of recommender systems in the healthy food domain. Journal of Intelligent Information Systems, 50, 501–526. DOI: 10.1007/s10844-017-0469-0
Tran, T. N. T., Felfernig, A., Trattner, C., et al. (2021). Recommender systems in the healthcare domain: State-of-the-art and research issues. Journal of Intelligent Information Systems, 57(1), 171–201. DOI: 10.1007/s10844-020-00633-6
Ueda, M., Takahata, M., & Nakajima, S. (2011). User’s food preference extraction for personalized cooking recipe recommendation. In Workshop of ISWC, Citeseer (pp. 98–105).
Ueda, M., Asanuma, S., Miyawaki, Y., & Nakajima, S. (2014). Recipe recommendation method by considering the users preference and ingredient quantity of target recipe. In Proceedings of the international multiconference of engineers and computer scientists (pp. 12–14).
Vultureanu-Albişi, A., & Bădică, C. (2021). Recommender systems: An explainable AI perspective. In 2021 International conference on innovations in intelligent systems and applications (INISTA) (pp. 1–6).
van der Waa, J., Nieuwburg, E., Cremers, A., et al. (2021). Evaluating Xai: A comparison of rule-based and example-based explanations. Artificial Intelligence, 291, 103404. DOI: 10.1016/j.artint.2020.103404
Wang, L., Rodriguez-Dominguez, R. M., & Wang, Y. M. (2018). A dynamic multi-attribute group emergency decision making method considering experts’ hesitation. International Journal of Computational Intelligence Systems, 11, 6. DOI: 10.2991/ijcis.11.1.13
Xu, Y., Collenette, J., Dennis, L., & Dixon, C. (2022). Dialogue-based explanations of reasoning in rule-based systems. In 3rd Workshop on explainable logic-based knowledge representation.
Zhu, X., Wang, D., Pedrycz, W., et al. (2023). Fuzzy rule-based local surrogate models for black-box model explanation. IEEE Transactions on Fuzzy Systems, 31(6), 2056–2064. DOI: 10.1109/TFUZZ.2022.3218426
