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Characterize ingredient network for recipe suggestion

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Abstract

Cooking is an art and every time whenever someone steps in kitchen, thought comes in one’s mind is to cook recipe according to ingredient availability. This diverted our attention also and soon it became apparent that a reliable system is required for preparatory descriptive analysis of the ingredient-recipe preparation study. Recipe preparations are inextricably linked with ingredient availability. To achieve this objective, we have formed two networks: ingredient–ingredient network and recipe-ingredient Network. These two networks are prime source to suggest recipe which user can cook according to available resources with the help of social network analysis models and its analysis measures. Network based study and analysis is effectively able to suggest recipes as an effective alternative to infer cooking preferences. Social network analysis and its measures have been used to suggest best preferable recipe according to the availability of ingredients and suggest ingredients those complement each other. Results show the suggestion of alternate recipe harmonizing with available ingredient using network. The discussed recipe suggestion system outperforms as compared to common approaches as existing approaches use only recipe–ingredient relationship for suggestion.

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References

  1. Manhas J (2017) Initial framework for website design and development. Int J Inf Technol 9(4):363–375

    Google Scholar 

  2. Aggrawal N, Ahluwalia A, Khurana P, Arora A (2017) Brand analysis framework for online marketing: ranking web pages and analyzing popularity of brands on social media. Soc Netw Anal Min 7(1):21

    Article  Google Scholar 

  3. Aggrawal N, Arora A (2016) Visualization, analysis and structural pattern infusion of DBLP co-authorship network using Gephi. In: Next generation computing technologies (NGCT), 2016 2nd international conference on. IEEE, pp 494–500

  4. Ahn YY, Ahnert SE, Bagrow JP, Barabási AL (2011) Flavor network and the principles of food pairing. Sci Rep 1:196

    Article  Google Scholar 

  5. Ahnert SE (2013) Network analysis and data mining in food science: the emergence of computational gastronomy. Flavour 2:4

    Article  Google Scholar 

  6. Bilgin A, Hagras H, van Helvert J, Alghazzawi D (2016) A linear general type-2 fuzzy-logic-based computing with words approach for realizing an ambient intelligent platform for cooking recipe recommendation. IEEE Trans Fuzzy Syst 24(2):306–329

    Article  Google Scholar 

  7. Birch LL (1999) Development of food preferences. Ann Rev Nutr 19(1):41–62

    Article  Google Scholar 

  8. Brandt MJ, IJzerman H, Dijksterhuis A, Farach FJ, Geller J, Giner-Sorolla R, Grange JA, Perugini M, Spies JR, Van’t Veer A (2014) The replication recipe: what makes for a convincing replication? J Exp Soc Psychol 50:217–224

    Article  Google Scholar 

  9. Freyne J, Berkovsky S, Smith G (2011) Recipe recommendation: accuracy and reasoning. International conference on user modeling, adaptation, and personalization. Springer, Berlin, Heidelberg, pp 99–110

    Chapter  Google Scholar 

  10. Gutiérrez F, Cardoso B, Verbert K (2017) PHARA: a personal health augmented reality assistant to support decision-making at grocery stores. In: Proceedings of the international workshop on health recommender systems co-located with ACM RecSys, pp 1–4

  11. Hammond KJ (1986) CHEF: a model of case-based planning. In: AAAI, pp 267–271

  12. Harvey M, Elsweiler D (2015) Automated recommendation of healthy, personalised meal plans. In: Proceedings of the 9th ACM conference on recommender systems. ACM, New York, pp 327–328

    Chapter  Google Scholar 

  13. Hu Y, Zhang J, Bai X, Yu S, Yang Z (2016) Influence analysis of Github repositories. SpringerPlus 5(1):1268

    Article  Google Scholar 

  14. Kumar RRV, Kumar MA, Soman KP (2016) Cuisine prediction based on ingredients using tree boosting algorithms. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i45/106484

    Article  Google Scholar 

  15. Liu XY, Chien BC (2017) Applying citation network analysis on recommendation of research paper collection. In: Proceedings of the 4th multidisciplinary international social networks conference on ZZZ. ACM, New York

    Google Scholar 

  16. Müller, M. M. (2012). Ingredient matching to determine the nutritional properties of internet-sourced recipes. In: 6th International Conf In Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. pp. 73-80). IEEE

  17. Ozaki T, Gao X, Mizutani M (2017) Extraction of characteristic sets of ingredients and cooking actions on cuisine type. In: Advanced information networking and applications workshops (WAINA), 2017 31st international conference on. IEEE, pp 509–513

  18. Potter NN, Hotchkiss JH (2012) Food science. Springer Science & Business Media, New York

    Google Scholar 

  19. She J, Vassilovski A, Hon A (2012) What cuisine do you like?: improving dining preference prediction through physical social locations. In: Green computing and communications (GreenCom), 2012 IEEE International Conference on. IEEE, pp 454–457

  20. Simas T, Ficek M, Diaz-Guilera A, Obrador P, Rodriguez PR (2017) Food-bridging: a new network construction to unveil the principles of cooking. Front ICT 4:14

    Article  Google Scholar 

  21. Teng CY, Lin YR, Adamic LA (2012) Recipe recommendation using ingredient networks. In: Proceedings of the 4th annual ACM web science conference. ACM, New York, pp 298–307

    Chapter  Google Scholar 

  22. Tran TNT, Atas M, Felfernig A, Stettinger M (2017) An overview of recommender systems in the healthy food domain. J Intell Inf Syst 50(3):501–526. https://doi.org/10.1007/s10844-017-0469-0

    Article  Google Scholar 

  23. Trattner C, Elsweiler D (2017) Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In: Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, pp 489–498

  24. Ueda M, Takahata M, Nakajima S (2011) User’s food preference extraction for personalized cooking recipe recommendation. In: Workshop of ISWC, pp 98–105

  25. Yu L, Li Q, Xie H, Cai Y (2011) Exploring folksonomy and cooking procedures to boost cooking recipe recommendation. In: Asia-Pacific web conference. Springer, Berlin, Heidelberg, pp 119–130

    Google Scholar 

  26. Khan I, Naqvi SK, Alam M, Rizvi SNA (2017) An efficient framework for real-time tweet classification. Int J Inf Technol 9(2):215–221

    Google Scholar 

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Authors and Affiliations

  1. Jaypee Institute of Information Technology, A-10, Sector 62, Noida, India

    Umang Nyati, Sneha Rawat, Devika Gupta, Niyati Aggrawal & Anuja Arora

Authors
  1. Umang Nyati
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  2. Sneha Rawat
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  3. Devika Gupta
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  4. Niyati Aggrawal
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  5. Anuja Arora
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Correspondence to Niyati Aggrawal.

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Nyati, U., Rawat, S., Gupta, D. et al. Characterize ingredient network for recipe suggestion. Int. j. inf. tecnol. 13, 2323–2330 (2021). https://doi.org/10.1007/s41870-019-00277-y

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  • DOI: https://doi.org/10.1007/s41870-019-00277-y

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