Skip to main content
SpringerLink
Log in

Food Recommendation System Based on Data Clustering Techniques and User Nutrition Records

  • Conference paper
  • First Online:
New Trends in Information and Communications Technology Applications (NTICT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1764))

  • 145 Accesses

Abstract

Food plays a vital role in the daily life; everybody needs a balanced diet to maintain a healthy body. Nowadays, the list of foods is continue growing. Some are natural, others are artificial, and they are reengineered and emerging every day, so choosing healthy food is becoming a more complex task. The food recommendation system (FRS) is a response to this need. It is used to give a recommended list of appropriate meal content that suits every client, and a website is built to solve part of this problem. RS helps users to find the right food groups. This paper gives an overview of RS types and defines FRS. In order to obtain a reasonable impression of the power and challenges of the RS field, a powerful model has been chosen, called the Gaussian Mixture Model GMM. The model clusters the dataset into 20 clusters. Some of them are more coherent and highly similar, while others have less similarity value. The use of Euclidean Distance and Manhattan Distance produced a similar recommended list according to user preferences depending on food nutrition. Also, using Cosine Similarity and Correlation will obtain a slight difference between its results and the first two algorithms. It’s clear that the Euclidean distance outperforms the other methods.

The current research presents three extra parameters certainty, exceeded, and outlier detection, not dealt by the previous related works which are representing data confidence, overweight, outliers consequently.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baek, J., Kim, J., Chun, J., Chung, K.: Hybrid clustering based health decision-making for improving dietary habits, vol. 1,  pp. 1–14 (January 2019).  https://doi.org/10.3233/THC-191730

  2. Martinon, P., Fraticelli, L., Giboreau, A., Dussart, C., Bourgeois, D., Carrouel, F.: Nutrition as a key modifiable factor for periodontitis and main chronic diseases. J. Clin. Med. 10(2), 1–26 (2021). https://doi.org/10.3390/jcm10020197

    Article  Google Scholar 

  3. Egan, A.M., Dinneen, S.F.: What is diabetes? Med. (United Kingdom) 47(1), 1–4 (2019). https://doi.org/10.1016/j.mpmed.2018.10.002

    Article  Google Scholar 

  4. Heba Abdelgader Mohammed, H.H.: Towards Developing Type 2 Fuzzy Logic Diet Recommendation System for Diabetes. In: 2018 10th Computer Science and Electronic Engineering , pp. 56–59 (2018).  https://doi.org/10.1109/CEEC.2018.8674186

  5. Batta, M.: Machine learning algorithms - a review. Int. J. Sci. Res. (IJ,  9(1),  381–386 (2020).  https://doi.org/10.21275/ART20203995

  6. Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: A systematic review. Expert Syst. Appl. 97, 205–227 (2018). https://doi.org/10.1016/j.eswa.2017.12.020

    Article  Google Scholar 

  7. Kumar, B.: Approaches, issues and challenges in recommender systems: a systematic review. Indian J. Sci. Technol. 9(1), 1–12 (2016). https://doi.org/10.17485/ijst/2016/v9i47/94892

    Article  Google Scholar 

  8. Liu, J., Duan, L.: A Survey on knowledge graph-based recommender systems. In: IEEE Advanced Information Technology, Electronic and Automation Control Conference, pp. 2450–2453 (2021). https://doi.org/10.1109/IAEAC50856.2021.9390863

  9. Malik, S., Rana, A., Bansal, M.: A survey of recommendation systems: recommendation models, techniques, and application fields. Inf. Resour. Manag. J. 33(4), 53–73 (2020). https://doi.org/10.4018/IRMJ.2020100104

    Article  Google Scholar 

  10. Wang, N., Zhao, H., Zhu, X., Li, N.: The review of recommendation system. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds.) GSES 2018. CCIS, vol. 980, pp. 332–342. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-7025-0_34

    Chapter  Google Scholar 

  11. Ali, S.I.M., Majeed, S.S.: A review of collaborative filtering recommendation system. Muthanna J. Pure Sci. 8(1), 120–131 (2021). https://doi.org/10.52113/2/08.01.2021/120-131

    Article  Google Scholar 

  12. Bobadilla, J., Bojorque, R., Esteban, A.H., Hurtado, R.: Recommender systems clustering using Bayesian non negative matrix factorization. IEEE Access 6, 3549–3564 (2017). https://doi.org/10.1109/ACCESS.2017.2788138

    Article  Google Scholar 

  13. Jain, H., Anika, M.: Applying Data Mining Techniques in MOOC Recommender System for Generating Course Recommendations (2017)

    Google Scholar 

  14. Priyanka, P.: A survey paper on various algorithm’s based recommender system. IOSR J. Comput. Eng. 19(3), 27–32 (2017). https://doi.org/10.9790/0661-1903042732

    Article  Google Scholar 

  15. Sun, Y., Zhang, Y.:  Conversational recommender system.  In: 41st Int. ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, pp. 235–244 (2018).  https://doi.org/10.1145/3209978.3210002

  16. Kumar, M.V., Kumar, P.N.V.S.P.: A study on different phases and various recommendation system techniques. Int. J. Recent Technol. Eng. 7(5), 38–41 (2019)

    Google Scholar 

  17. Trattner, C., Elsweiler, D.: Food Recommender Systems: Important Contributions, Challenges and Future Research Directions (November 2017).  http://arxiv.org/abs/1711.02760

  18. Phorasim, P., Yu, L.: Movies recommendation system using collaborative filtering and k-means. Int. J. Adv. Comput. Res. 7(29), 52–59 (2017). https://doi.org/10.19101/IJACR.2017.729004

    Article  Google Scholar 

  19. Jaiswal, V.: A new approach for recommending healthy diet using predictive data mining algorithm. Int. J. Res. Anal. Rev. 6(1), 58–66 (2019).  www.ijrar.org

  20. Fkih, F.: Similarity measures for collaborative filtering-based recommender systems: review and experimental comparison. J. King Saud Univ. - Comput. Inf. Sci.  (2021).  https://doi.org/10.1016/j.jksuci.2021.09.014

  21. Thorat, P.B., Goudar, R.M., Barve, S.: Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications 110(4), 31–36 (2015). https://doi.org/10.5120/19308-0760

    Article  Google Scholar 

  22. Shah, K., Salunke, A., Dongare, S., Antala, K.: Recommender systems: An overview of differ K.  ent approaches to recommendations. In: Proceedings of 2017 International Conference on Innovations in Information, Embedded and Communication Systems,  ICIIECS 2017, vol. 2018, pp. 1–4 (2018).  https://doi.org/10.1109/ICIIECS.2017.8276172

  23. Premasundari, M., Yamini, C.: Food and therapy recommendation system for autistic syndrome using machine learning techniques. In: Proceedings of 2019 3rd  IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, pp. 1–6 (2019).  https://doi.org/10.1109/ICECCT.2019.8868979

  24. Shah, H., Napanda, K., Lynette, D.: Density based clustering algorithms. Int. J. Comput. Sci. Eng. 3(11), 54–57 (2015)

    Google Scholar 

  25. Lopez-Arevalo, I., Aldana-Bobadilla, E., Molina-Villegas, A., Galeana-Zapién, H., Muñiz-Sanchez, V., Gausin-Valle, S.: A memory-efficient encoding method for processing mixed-type data on machine learning. Entropy 22(12), 1–21 (2020). https://doi.org/10.3390/e22121391

    Article  MathSciNet  Google Scholar 

  26. Wang, T.,  Ren, C., Luo, Y., Tian, J.: NS-DBSCAN: A density-based clustering algorithm in network space. ISPRS Int. J. Geo-Information  8(5) (2019).  https://doi.org/10.3390/ijgi8050218

  27. Doroshenko, A.: Analysis of the distribution of COVID-19 in Italy using clustering algorithms. In: Proceedings of 2020 IEEE 3rd  International Conference on Data Stream Mining & Processing, DSMP 2020, pp. 325–328 (2020).  https://doi.org/10.1109/DSMP47368.2020.9204202

  28. Zhu, X., Li, Y., Wang, J., Zheng, T., Fu, J.: Automatic recommendation of a distance measure for clustering algorithms. ACM Trans. Knowl. Discov. Data 15(1), 1–22 (2021). https://doi.org/10.1145/3418228

    Article  Google Scholar 

  29. Cai, X., Hu, Z., Zhao, P., Zhang, W.S., Chen, J.: A hybrid recommendation system with many-objective evolutionary algorithm. Expert Syst. Appl. 159, 113648 (2020). https://doi.org/10.1016/j.eswa.2020.113648

    Article  Google Scholar 

  30. Koosha, H., Ghorbani, Z., Nikfetrat, R.: A Clustering-Classification Recommender System based on Firefly Algorithm (2021).  https://doi.org/10.22044/JADM.2021.10782.2216

  31. Patil, P., Karthikeyan, A.: A survey on k-means clustering for analyzing variation in data. In: Ranganathan, G., Chen, J., Rocha, Á. (eds.) Inventive Communication and Computational Technologies. LNNS, vol. 89, pp. 317–323. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0146-3_29

    Chapter  Google Scholar 

  32. Borlea, I., Precup, R., Borlea, A.: improvement of k-means cluster quality by post processing resulted clusters. Procedia Comput. Sci. 199, 63–70 (2022). https://doi.org/10.1016/j.procs.2022.01.009

    Article  Google Scholar 

  33. Xu, W.,  Li, Y.,  Wei, L., Wu, Z.: A feature-based food recommendation on apache spark. In: Proceedings of 5th International Conference on Advanced Cloud Big Data, CBD 2017, pp. 123–128 (2017).  https://doi.org/10.1109/CBD.2017.29

  34. Elsweiler, D., Harvey, M.: Towards automatic meal plan recommendations for balanced nutrition. In: RecSys 2015 - Proceedings 9th ACM Conference on Recommender Systems, pp. 313–316 (2015).  https://doi.org/10.1145/2792838.2799665

  35. Min, W., Jiang, S., Liu, L., Rui, Y., Jain, R.: A survey on food computing. ACM Comput. Surv.,  52(5) (2019).  https://doi.org/10.1145/3329168

  36.  Banerjee, A., Nigar, N.: Nourishment recommendation framework for children using machine learning and matching algorithm. In: 2019 International Conference on Computer Communication and Informatics, ICCCI 2019, pp. 1–6 (2019).  https://doi.org/10.1109/ICCCI.2019.8822102

  37. Ribeiro, D., Machado, J., Ribeiro, J.,  Vasconcelos, M.J.M., Vieira, E.F.,  De Barros, A.C.: SousChef: Mobile meal recommender system for older adults. In: ICT4AWE 2017 - Proceedings of 3rd  International Conference on Information and Communication Technologies for Ageing Well and e-Health,  Ict4awe, pp. 36–45 (2017).  https://doi.org/10.5220/0006281900360045

  38. Raja, P.S., Thangavel, K.: Missing value imputation using unsupervised machine learning techniques, vol. 24(6). Springer, Berlin Heidelberg (2020)

    Google Scholar 

  39. FAO, The State of Food and Agriculture: Agricultural Trade and Poverty, FAO Agric. Ser., vol. 36(0081–4539), p. 211 (2005)

    Google Scholar 

  40. Raju, V.N.G., Lakshmi, K.P., Jain, V.M.,  Kalidindi, A., Padma, V.: Study the influence of normalization/transformation process on the accuracy of supervised Classification,” Proc. 3rd  International Conference on Smart Systems and Inventive Technology, ICSSIT 2020,  Icssit, pp. 729–735 (2020). https://doi.org/10.1109/ICSSIT48917.2020.9214160

  41. Dina, N., Kashef, R.: Deploying Different Clustering Techniques on a Collaborative-based Movie Recommender. p. 6 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Informatics Institute for Postgraduate Studies (IIPS), Baghdad, Iraq

    Hayder Hussein Al-Chalabi

  2. Al-Farahidi University, Baghdad, Iraq

    Hayder Hussein Al-Chalabi

  3. University of Information Technology and Communications, Baghdad, Iraq

    Mahdi Nsaif Jasim

Authors
  1. Hayder Hussein Al-Chalabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahdi Nsaif Jasim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahdi Nsaif Jasim .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Safaa O. Al-Mamory

  3. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  4. University of Information Technology and Communications, Baghdad, Iraq

    Loay E. George

  5. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. Open University of Tanzania, Dar es Salaam, Tanzania

    George S. Oreku

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al-Chalabi, H.H., Jasim, M.N. (2023). Food Recommendation System Based on Data Clustering Techniques and User Nutrition Records. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2022. Communications in Computer and Information Science, vol 1764. Springer, Cham. https://doi.org/10.1007/978-3-031-35442-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35442-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35441-0

  • Online ISBN: 978-3-031-35442-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation