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FoodRecNet: a comprehensively personalized food recommender system using deep neural networks

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Abstract

Today, the huge variety of foods and the existence of different food preferences among people have made it difficult to choose the right food according to people's food preferences for different meals. Also, achieving a pleasant balance between users’ food preferences and health requirements, considering the physical condition, diseases/allergies of users, and having a suitable dietary diversity, has become a requirement in the field of nutrition. Therefore, the need for an intelligent system to recommend and choose the proper food based on these criteria is felt more and more. In this paper, a deep learning-based food recommender system, termed “FoodRecNet”, is presented using a comprehensive set of characteristics and features of users and foods, including users’ long-term and short-term preferences, users’ health conditions, demographic information, culture, religion, food ingredients, type of cooking, food category, food tags, diet, allergies, text description, and even the images of the foods. The appropriate combination of features used in the proposed system has been identified based on detailed investigations conducted in this research. In order to achieve a desired architecture of the deep artificial neural network for our purpose, five different architectures are designed and evaluated, considering the specific characteristics of the intended application In addition, to evaluate the FoodRecNet, this work constructs a large-scale annotated dataset, consisting of 3,335,492 records of food information, users and their scores, and 54,554 food images. The experiments conducted on this dataset and the “FOOD.COM” benchmark dataset confirm the effectiveness of the combination of features used in FoodRecNet. The RMSE rates obtained by FoodRecNet on these two datasets are 0.7167 and 0.4930, respectively, which are much better than that of competitors. All the implementation source codes and datasets of this research are made publicly available at https://github.com/saeedhamdollahi/FoodRecNet.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://www.allrecipes.com/.

  2. https://en.wikipedia.org/wiki/Body_mass_index.

  3. https://www.python.org/.

  4. https://keras.io/.

  5. https://www.tensorflow.org/.

References

  1. Dong M, Zeng X, Koehl L, Zhang J (2020) An interactive knowledge-based recommender system for fashion product design in the big data environment. Inf Sci 540:469–488. https://doi.org/10.1016/j.ins.2020.05.094

    Article  Google Scholar 

  2. Katarya R, Saini R (2022) Enhancing the wine tasting experience using greedy clustering wine recommender system. Multimedia Tools Appl 81(1):807–840. https://doi.org/10.1007/s11042-021-11300-5

    Article  Google Scholar 

  3. Orciuoli F, Parente M (2017) An ontology-driven context-aware recommender system for indoor shopping based on cellular automata. J Ambient Intell Humaniz Comput 8(6):937–955. https://doi.org/10.1007/s12652-016-0411-2

    Article  Google Scholar 

  4. Cao J, Wu Z, Wang Y, Zhuang Y (2013) Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation. Knowl Inf Syst 36(3):607–627. https://doi.org/10.1007/s10115-012-0562-1

    Article  Google Scholar 

  5. Christou IT, Amolochitis E, Tan Z-H (2016) AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine. Knowl Inf Syst 47(3):671–696. https://doi.org/10.1007/s10115-015-0866-z

    Article  Google Scholar 

  6. Dehghani Champiri Z, Asemi A, Siti Salwah Binti S (2019) Meta-analysis of evaluation methods and metrics used in context-aware scholarly recommender systems. Knowl Inf Syst 61(2):1147–1178. https://doi.org/10.1007/s10115-018-1324-5

    Article  Google Scholar 

  7. Liang T et al (2020) CAMAR: a broad learning based context-aware recommender for mobile applications. Knowl Inf Syst 62(8):3291–3319. https://doi.org/10.1007/s10115-020-01440-9

    Article  MathSciNet  Google Scholar 

  8. Zhao WX, Li S, He Y, Wang L, Wen J-R, Li X (2016) Exploring demographic information in social media for product recommendation. Knowl Inf Syst 49(1):61–89. https://doi.org/10.1007/s10115-015-0897-5

    Article  Google Scholar 

  9. Silva TH, Vaz de Melo POS, Almeida JM, Musolesi M, Loureiro AAF (2017) A large-scale study of cultural differences using urban data about eating and drinking preferences. Inf Syst 72:95–116. https://doi.org/10.1016/j.is.2017.10.002

    Article  Google Scholar 

  10. Parker AG, Grinter RE (2014) Collectivistic health promotion tools: accounting for the relationship between culture, food and nutrition. Int J Hum Comput Stud 72(2):185–206. https://doi.org/10.1016/j.ijhcs.2013.08.008

    Article  Google Scholar 

  11. Hauptmann H et al (2021) Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study. User Model User-Adapt Interact. https://doi.org/10.1007/s11257-021-09301-y

    Article  Google Scholar 

  12. Tran TNT, Atas M, Felfernig A, Stettinger M (2018) An overview of recommender systems in the healthy food domain. J Intell Inf Syst:1–26

  13. Kant S, Mahara T (2018) Nearest biclusters collaborative filtering framework with fusion. J Comput Sci 25:204–212. https://doi.org/10.1016/j.jocs.2017.03.018

    Article  Google Scholar 

  14. Freyne J, Berkovsky S (2010) Intelligent food planning: personalized recipe recommendation. In: Presented at the Proceedings of the 15th international conference on Intelligent user interfaces, Hong Kong, China

  15. Teng C-Y, Lin Y-R, Adamic LA (2012) Recipe recommendation using ingredient networks. In: Proceedings of the 4th annual ACM web science conference. ACM, pp 298–307

  16. Lin C-J, Kuo T-T, Lin S-D (2014) A content-based matrix factorization model for recipe recommendation. In: Advances in knowledge discovery and data mining. Springer, Cham, pp 560–571

  17. de Almeida JMTS (2015) Personalized food recommendations

  18. Ge M, Elahi M, Fernaández-Tobías I, Ricci F, Massimo D (2015) Using tags and latent factors in a food recommender system. In: Proceedings of the 5th international conference on digital health 2015. ACM, pp 105–112

  19. Bianchini D, De Antonellis V, De Franceschi N, Melchiori M (2017) PREFer: a prescription-based food recommender system. Comput Stand Interfaces 54:64–75

    Article  Google Scholar 

  20. Mokdara T, Pusawiro P, Harnsomburana J (2018) Personalized food recommendation using deep neural network. In: 2018 Seventh ICT international student project conference (ICT-ISPC). IEEE, pp 1–4

  21. Suksom N, Buranarach M, Thein YM, Supnithi T, Netisopakul P (2010) A knowledge-based framework for development of personalized food recommender system. In: Proc. of the 5th Int. Conf. on Knowledge, Information and Creativity Support Systems

  22. Agapito G et al (2018) DIETOS: a dietary recommender system for chronic diseases monitoring and management. Comput Methods Prog Biomed 153:93–104

    Article  Google Scholar 

  23. El-Dosuky MA, Rashad MZ, Hamza TT, EL-Bassiouny AH (2012) Food recommendation using ontology and heuristics. In: Advanced machine learning technologies and applications. Springer, Berlin, pp 423–429

  24. Elsweiler D, Harvey M, Ludwig B, Said A (2015) Bringing the" healthy" into food recommenders. In: DMRS, pp 33–36

  25. Twomey N, Fain M, Ponikar A, Sarraf N (2020) Towards multi-language recipe personalisation and recommendation. In: Presented at the fourteenth ACM conference on recommender systems, virtual event, Brazil, [Online]. https://doi.org/10.1145/3383313.3418478

  26. Kilani Y, Otoom AF, Alsarhan A, Almaayah M (2018) A genetic algorithms-based hybrid recommender system of matrix factorization and neighborhood-based techniques. J Comput Sci 28:78–93. https://doi.org/10.1016/j.jocs.2018.08.007

    Article  Google Scholar 

  27. Vairale VS, Shukla S (2021) Recommendation of food items for thyroid patients using content-based KNN method. In: Data science and security, Springer, Singapore, pp 71–77

  28. Li H, Li K, An J, Zheng W, Li K (2019) An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs. Inf Sci 496:464–484. https://doi.org/10.1016/j.ins.2018.07.060

    Article  Google Scholar 

  29. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press

    MATH  Google Scholar 

  30. Yang L, Cui Y, Zhang F, Pollak JP, Belongie S, Estrin D (2015) PlateClick: bootstrapping food preferences through an adaptive visual interface. In: Presented at the proceedings of the 24th ACM international on conference on information and knowledge management, Melbourne, Australia, [Online]. https://doi.org/10.1145/2806416.2806544

  31. Yang L et al (2017) Yum-me: a personalized nutrient-based meal recommender system. ACM Trans Inf Syst 36(1):7. https://doi.org/10.1145/3072614

    Article  Google Scholar 

  32. Gao X et al (2020) Hierarchical attention network for visually-aware food recommendation. IEEE Trans Multimedia 22:1647–1659. https://doi.org/10.1109/TMM.2019.2945180

    Article  MathSciNet  Google Scholar 

  33. Meng L, Feng F, He X, Gao X, Chua T-S (2020) Heterogeneous fusion of semantic and collaborative information for visually-aware food recommendation. In: Presented at the Proceedings of the 28th ACM international conference on multimedia, Seattle, WA, USA, [Online]. https://doi.org/10.1145/3394171.3413598

  34. Son LH (2016) Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf Syst 58:87–104. https://doi.org/10.1016/j.is.2014.10.001

    Article  Google Scholar 

  35. Bernardis C, Cremonesi P (2021) NFC: a deep and hybrid item-based model for item cold-start recommendation. User Model User-Adapted Interact. https://doi.org/10.1007/s11257-021-09303-w

    Article  Google Scholar 

  36. Eftimov T, Popovski G, Petković M, Seljak BK, Kocev D (2020) COVID-19 pandemic changes the food consumption patterns. Trends Food Sci Technol 104:268–272. https://doi.org/10.1016/j.tifs.2020.08.017

    Article  Google Scholar 

  37. Groves S (2013) How allrecipes. com became the worlds largest food/recipe site. roi of social media (blog). Tech. Rep

  38. Popovski G, Seljak BK, Eftimov T (2020) A survey of named-entity recognition methods for food information extraction. IEEE Access 8:31586–31594. https://doi.org/10.1109/ACCESS.2020.2973502

    Article  Google Scholar 

  39. Chen M, Jia X, Gorbonos E, Hoang CT, Yu X, Liu Y (2020) Eating healthier: exploring nutrition information for healthier recipe recommendation. Inf Process Manag 57(6):102051. https://doi.org/10.1016/j.ipm.2019.05.012

    Article  Google Scholar 

  40. Deng S, Wang D, Li X, Xu G (2015) Exploring user emotion in microblogs for music recommendation. Expert Syst Appl 42(23):9284–9293

    Article  Google Scholar 

  41. Zhu B, Ortega F, Bobadilla J, Gutiérrez A (2018) Assigning reliability values to recommendations using matrix factorization. J Comput Sci 26:165–177. https://doi.org/10.1016/j.jocs.2018.04.009

    Article  Google Scholar 

  42. Alhijawi B, Al-Naymat G, Obeid N, Awajan A (2021) Novel predictive model to improve the accuracy of collaborative filtering recommender systems. Inf Syst 96:101670. https://doi.org/10.1016/j.is.2020.101670

    Article  Google Scholar 

  43. Fernández-Tobías I, Cantador I, Tomeo P, Anelli VW, Di Noia T (2019) Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. User Model User-Adap Inter 29(2):443–486. https://doi.org/10.1007/s11257-018-9217-6

    Article  Google Scholar 

  44. Papadakis H, Papagrigoriou A, Panagiotakis C, Kosmas E, Fragopoulou P (2022) Collaborative filtering recommender systems taxonomy. Knowl Inf Syst 64(1):35–74. https://doi.org/10.1007/s10115-021-01628-7

    Article  Google Scholar 

  45. Anwaar F, Iltaf N, Afzal H, Nawaz R (2018) HRS-CE: A hybrid framework to integrate content embeddings in recommender systems for cold start items. J Comput Sci 29:9–18. https://doi.org/10.1016/j.jocs.2018.09.008

    Article  Google Scholar 

  46. Ricci F, Rokach L, Shapira B (2015) Recommender systems: introduction and challenges. In: Recommender systems handbook. Springer, pp 1–34

  47. Li X et al (2018) Application of intelligent recommendation techniques for consumers’ food choices in restaurants. Front Psychiatry. https://doi.org/10.3389/fpsyt.2018.00415

    Article  Google Scholar 

  48. Elsweiler D, Trattner C, Harvey M (2017) Exploiting food choice biases for healthier recipe recommendation. In: Presented at the proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Shinjuku, Tokyo, Japan, [Online]. https://doi.org/10.1145/3077136.3080826

  49. Health.gov. Estimated Calorie Needs per Day, by Age, Sex, and Physical Activity Level. https://health.gov/our-work/food-nutrition/2015-2020-dietary-guidelines/guidelines/appendix-2/

  50. Health.gov. Nutritional Goals for Age-Sex Groups Based on Dietary Reference Intakes and Dietary Guidelines Recommendations. https://health.gov/our-work/food-nutrition/2015-2020-dietary-guidelines/guidelines/appendix-7/

  51. Maia R, Ferreira JC (2018) Context-aware food recommendation system. In: Proceedings of the world congress on engineering and computer science, San Francisco, USA, vol. I: International Association of Engineers, pp 349–356

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Funding

The authors declare that no funds, grants, or other support was received during the preparation of this manuscript.

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Authors and Affiliations

  1. Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

    Saeed Hamdollahi Oskouei & Mahdi Hashemzadeh

  2. Artificial Intelligence and Machine Learning Research Laboratory, Azarbaijan Shahid Madani University, Tabriz, Iran

    Saeed Hamdollahi Oskouei & Mahdi Hashemzadeh

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  1. Saeed Hamdollahi Oskouei
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  2. Mahdi Hashemzadeh
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Contributions

SHO contributed to the conceptualization; data curation; formal analysis; investigation; methodology; validation; visualization; writing—original draft; writing—review and editing. MH was involved in the conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; supervision; validation; visualization; writing—review & editing.

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Correspondence to Mahdi Hashemzadeh.

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Hamdollahi Oskouei, S., Hashemzadeh, M. FoodRecNet: a comprehensively personalized food recommender system using deep neural networks. Knowl Inf Syst 65, 3753–3775 (2023). https://doi.org/10.1007/s10115-023-01897-4

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