Hybrid Filtering for Personalized and Health-Conscious Recipe Recommendations in UniEats
Article Sidebar
Main Article Content
Abstract
This research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through dashboards, and automatic grocery list generation. By enabling students to explore diverse culinary options, create balanced meal plans, and understand the nutritional content of their meals, UniEats empowers them to make informed dietary decisions. This research paper discusses the project's background, motivation, and objectives, emphasizing the importance of addressing students' dietary challenges. It also reviews existing recommendation systems and algorithms, justifying the choice of hybrid filtering for personalized meal suggestions. Additionally, the research paper details the system's design, implementation, and testing procedures, highlighting the development process. UniEats is a practical solution that leverages machine learning and data-driven methods to enhance students' culinary experiences, support skill development, and promote nutritional awareness. By tackling key challenges in meal planning and healthy eating, UniEats aims to improve the overall well-being of university students.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
P.A. Chavan, “FeedMeRight-Recipe Recommendation System,” M.S. thesis, California State Univ., Monterey Bay, CA, USA, 2020. [Online]. Available: https://scholarworks.calstate.edu/concern/theses/pg15bg98f.
J. Garcia, “The Lack of Nutrition Education among Student Athletes,” Capstone Project, California State Univ., Monterey Bay, CA, USA, 2018. [Online]. Available: https://digitalcommons.csumb.edu/caps_thes_all.
J.E. Pelletier, and M.N. Laska, “Balancing healthy meals and busy lives: Associations between work, school, and family responsibilities and perceived time constraints among young adults,” J. Nutr. Educ. Behav., vol. 44, no. 6, pp. 481–489, Nov. 2012, doi: 10.1016/j.jneb.2012.04.001.
M. Telfer, “An analysis of stress and dietary intake among university students,” J. Amer. Coll. Health, vol. 69, no. 5, pp. 556–560, 2021, doi: 10.1080/07448481.2019.1686005.
News-Medical.net, “Are inadequate culinary skills among undergraduate students associated with obesity and overweight?,” May 26, 2023. [Online]. Available: https://www.news-medical.net/news/20230526/Are-inadequate-culinary-skills-among-undergraduate-students-associated-with-obesity-and-overweight.aspx. [Accessed: Aug. 25, 2025].
L.G. Snetselaar et al., “Dietary guidelines for Americans, 2020–2025,” Nutr. Today, vol. 56, no. 6, pp. 287–295, Nov. 2021, doi: 10.1097/NT.0000000000000512.
S. Mills, S. Brown, W. Wrieden, M. White, and J. Adams, “Frequency of eating home cooked meals and potential benefits for diet and health: Cross-sectional analysis of a population-based cohort study,” Int. J. Behav. Nutr. Phys. Act., vol. 14, no. 1, p. 109, Aug. 2017, doi: 10.1186/s12966-017-0567-y.
J. Wardle, K. Parmenter, and J. Waller, “Nutrition knowledge and food intake,” Appetite, vol. 34, no. 3, pp. 269–275, Jun. 2000, doi: 10.1006/appe.1999.0311.
W. Husain, F. Ashkanani, and M.A. Al-Dwairji, “Nutrition knowledge among college of basic education students in Kuwait: A cross-sectional study,” J. Nutr. Metab, Art. no. 5560714, 2021, doi: 10.1155/2021/5560714.
I.B. Bobadilla, and M.K. Krasnorylov, “A review of clustering-based recommender systems,” IEEE Access, vol. 9, pp. 145, 2021, doi: 10.1109/ACCESS.2021.3131265.
G.M.P, “Research on recommendation systems using deep learning models,” Int. J. Rec. Technol. Eng., vol. 8, no. 4, pp. 456–460, Jan. 2022, doi: 10.35940/ijrte.D4609.118419.
J.A. Wolfson, and S.N. Bleich, “Is cooking at home associated with better diet quality or weight-loss intention?,” Public Health Nutr., vol. 18, no. 8, pp. 1397–1406, Jun. 2015, doi: 10.1017/S1368980014001943.
F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston, MA, USA: Springer, 2011, pp. 1–35.
M.J. Thornton, G.E. Adam, D.R. Black, and C.L. Adams, “The influence of time, motivation, and planning on meal preparation among university students,” J. Nutr. Educ. Behav., vol. 49, no. 7, p. S1–S2, Jul. 2017, doi: 10.1016/j.jneb.2017.05.340.
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Commun. ACM, vol. 35, no. 12, pp. 61–70, Dec. 1992, doi: 10.1145/138859.138867.
M.D. Abel, “A hybrid approach to recipe recommendation,” M.S. thesis, Dept. Comput. Sci., Univ. Colorado, Boulder, CO, USA, 2018. [Online]. Available: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/2n49t4443
M.F. Harper, and J.A. Konstan, “The MovieLens datasets: History and context,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, pp. 1–19, Dec. 2015, doi: 10.1145/2827872.
A.K.M.N. Islam, M.M. Rahman, and S.S. Islam, “A survey on food recommendation systems,” in Proc. 4th Int. Conf. Trends Electron. Informat. (ICOEI), Tirunelveli, India, pp. 728–733, 2020, doi: 10.1109/ICOEI48184.2020.9142988.
L. Hong, Y. Fu, and J. Liu, “Health-aware food recommendation system,” in Companion Proc. Web Conf. 2021, Ljubljana, Slovenia, pp. 25–26, 2021, doi: 10.1145/3442442.3452050.
A. Musto, C. Basile, P. Lops, and G. Semeraro, “Health-aware food recommendation: A model and a platform for the recommendation of recipes for a balanced diet,” IEEE Trans. Emerg. Top. Comput., vol. 10, no. 2, pp. 1054–1067, Apr.-Jun. 2022, doi: 10.1109/TETC.2021.3139364.
A. A. Freitas, T. R. Li, and J. T. Wang, “Recipe recommendation for a balanced diet: A multi-objective optimization approach,” J. Amer. Med. Inform. Assoc., vol. 30, no. 4, pp. 718–728, Mar. 2023, doi: 10.1093/jamia/ocad015.