Bargaining agents based system for automatic classification of potential allergens in recipes
Resumen The automatic recipe recommendation which take into account the dietary restrictions of users (such as allergies or intolerances) is a complex and open problem. Some of the limitations of the problem is the lack of food databases correctly labeled with its potential allergens and non-unification of this information by companies in the food sector. In the absence of an appropriate solution, people affected by food restrictions cannot use recommender systems, because this recommend them inappropriate recipes. In order to resolve this situation, in this article we propose a solution based on a collaborative multi-agent system, using negotiation and machine learning techniques, is able to detect and label potential allergens in recipes. The proposed system is being employed in receteame.com, a recipe recommendation system which includes persuasive technologies, which are interactive technologies aimed at changing users’ attitudes or behaviors through persuasion and social influence, and social information to improve the recommendations.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Adomavicius, G. and Tuzhilin, A., 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734–749.
Elsweiler, D. and Harvey, M., 2015. Towards automatic meal plan recommendations for balanced nutrition. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 313–316. ACM.
Freyne, J. and Berkovsky, S., 2010. Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces, pages 321–324. ACM. https://doi.org/10.1145/1719970.1720021
Hammond, K. J., 1989. Case-based Planning: Viewing Planning As a Memory Task. Academic Press Professional, Inc., San Diego, CA, USA. ISBN 0-12-322060-2. https://doi.org/10.1016/b978-0-12-322060-8.50018-8
Harvey, M., Ludwig, B., and Elsweiler, D., 2013. You are what you eat: Learning user tastes for rating prediction. In String Processing and Information Retrieval, pages 153–164. Springer. https://doi.org/10.1007/978-3-319-02432-5_19
Kolodner, J. L., 1987. Capitalizing on failure through case-based inference. Technical report, DTIC Document.
Mazzotta, I., De Rosis, F., and Carofiglio, V., 2007. Portia: A user-adapted persuasion system in the healthy-eating domain. Intelligent Systems, IEEE, 22(6):42–51. https://doi.org/10.1109/MIS.2007.115
Palanca, J., Heras, S., Botti, V., and Julian, V., 2014. receteame.com: a Persuasive Social Recommendation System. In 12th International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer. https://doi.org/10.1007/978-3-319-07551-8_40
Phanich, M., Pholkul, P., and Phimoltares, S., 2010. Food recommendation system using clustering analysis for diabetic patients. In Information Science and Applications (ICISA), 2010 International Conference on, pages 1–8. IEEE. https://doi.org/10.1109/icisa.2010.5480416
Schall, D., 2015. Social Network-Based Recommender Systems.
Teng, C.-Y., Lin, Y.-R., and Adamic, L. A., 2012. Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference, pages 298–307. ACM. https://doi.org/10.1145/2380718.2380757
Ueda, M., Takahata, M., and Nakajima, S., 2011. User's food preference extraction for personalized cooking recipe recommendation. Semantic Personalized Information Management: Retrieval and Recommendation SPIM 2011, page 98.
Van der Aalst, W. M. and Song, M., 2004. Mining Social Networks: Uncovering interaction patterns in business processes. In Business Process Management, pages 244–260. Springer.
Zhou, X., Xu, Y., Li, Y., Josang, A., and Cox, C., 2012. The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review, 37(2):119–132. https://doi.org/10.1007/s10462-011-9222-1
Elsweiler, D. and Harvey, M., 2015. Towards automatic meal plan recommendations for balanced nutrition. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 313–316. ACM.
Freyne, J. and Berkovsky, S., 2010. Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces, pages 321–324. ACM. https://doi.org/10.1145/1719970.1720021
Hammond, K. J., 1989. Case-based Planning: Viewing Planning As a Memory Task. Academic Press Professional, Inc., San Diego, CA, USA. ISBN 0-12-322060-2. https://doi.org/10.1016/b978-0-12-322060-8.50018-8
Harvey, M., Ludwig, B., and Elsweiler, D., 2013. You are what you eat: Learning user tastes for rating prediction. In String Processing and Information Retrieval, pages 153–164. Springer. https://doi.org/10.1007/978-3-319-02432-5_19
Kolodner, J. L., 1987. Capitalizing on failure through case-based inference. Technical report, DTIC Document.
Mazzotta, I., De Rosis, F., and Carofiglio, V., 2007. Portia: A user-adapted persuasion system in the healthy-eating domain. Intelligent Systems, IEEE, 22(6):42–51. https://doi.org/10.1109/MIS.2007.115
Palanca, J., Heras, S., Botti, V., and Julian, V., 2014. receteame.com: a Persuasive Social Recommendation System. In 12th International Conference on Practical Applications of Agents and Multi-Agent Systems. Springer. https://doi.org/10.1007/978-3-319-07551-8_40
Phanich, M., Pholkul, P., and Phimoltares, S., 2010. Food recommendation system using clustering analysis for diabetic patients. In Information Science and Applications (ICISA), 2010 International Conference on, pages 1–8. IEEE. https://doi.org/10.1109/icisa.2010.5480416
Schall, D., 2015. Social Network-Based Recommender Systems.
Teng, C.-Y., Lin, Y.-R., and Adamic, L. A., 2012. Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference, pages 298–307. ACM. https://doi.org/10.1145/2380718.2380757
Ueda, M., Takahata, M., and Nakajima, S., 2011. User's food preference extraction for personalized cooking recipe recommendation. Semantic Personalized Information Management: Retrieval and Recommendation SPIM 2011, page 98.
Van der Aalst, W. M. and Song, M., 2004. Mining Social Networks: Uncovering interaction patterns in business processes. In Business Process Management, pages 244–260. Springer.
Zhou, X., Xu, Y., Li, Y., Josang, A., and Cox, C., 2012. The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review, 37(2):119–132. https://doi.org/10.1007/s10462-011-9222-1
Alemany, J., Heras, S., Palanca, J., & Julián, V. (2016). Bargaining agents based system for automatic classification of potential allergens in recipes. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 5(2), 43–51. https://doi.org/10.14201/ADCAIJ2016524351
Artículos más leídos del mismo autor/a
- Cristian Peñaranda, Jorge Aguero, Carlos Carrascosa, Miguel Rebollo, Vicente Julián, An Agent-Based Approach for a Smart Transport System , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 5 Núm. 2 (2016)
- Angelo Costa, Stella Heras, Javier Palanca, Paulo Novais, Vicente Julián, Persuasion and Recommendation System Applied to a Cognitive Assistant , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 5 Núm. 2 (2016)
- Jaime Rincón, Jose Luis Poza, Juan Luis Posadas, Vicente Julián, Carlos Carrascosa, Adding real data to detect emotions by means of smart resource artifacts in MAS , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 5 Núm. 4 (2016)
- Vicente Julián, Martí Navarro, Vicente Botti, Stella Heras, Towards Real-Time Argumentation , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 4 Núm. 4 (2015)
- Jorge Agüero, Miguel Rebollo, Carlos Carrascosa, Vicente Julián, MDD-Approach for developing Pervasive Systems based on Service-Oriented Multi-Agent Systems , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 2 Núm. 3 (2013)
- Pasqual Martí, Alejandro Ibáñez, Vicente Julian, Paulo Novais, Jaume Jordán, Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 13 (2024)
Descargas
Los datos de descargas todavía no están disponibles.
+
−