Intelligent Food Recommendation Framework Based on Social Media Behavioral Data
Pages 124 - 129
Abstract
This research introduces an Intelligent Food Recommendation Framework leveraging social media Behavioral Data, explicitly focusing on Instagram. It posits that users' engagements with food-related content on Instagram offer insights into their food preferences, thereby enhancing the precision of food type recommendations. The investigation aims to substantiate this assertion through a thorough literature review, data source selection, comparison of food assessment surveys, exploration of image recognition methodologies, and deployment and validation of the proposed solution framework utilizing deep learning models. Through interdisciplinary efforts, the study seeks to advance personalized recommendation systems in the context of food consumption.
Supplemental Material
PPTX File
Presentation slides detailing an innovative Intelligent Food Recommendation Framework leveraging Social Media Behavioral Data. The framework employs advanced algorithms to analyze user interactions on social platforms, offering personalized food recommendations. Exploring the intersection of AI and social data, this research enhances culinary experiences by providing tailored suggestions, promising improved user engagement and satisfaction in food consumption.
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References
[1]
P. Resnick and H. R. Varian, Recommender Systems, vol. 40, no. 3. 1997.
[2]
"How we work," kayak, 2022. [Online]. Available:
[3]
https://www.kayak.ae/horizon/sem/flights/general?lang=en&skipapp=true&gclid=CjwKCAiArOqOBhBmEiwAsgeLmbLkwM3qLGCON8Fgu7feJJtq6mJsVwGqQ9gEBAByH_tu2SljBlPL8xoCfaYQAvD_BwE&g_kw=kayak. [Accessed: April 4,2024].
[4]
"Experience our experience, "flightcentre, 2024. [Online]. Available: https://www.flightcentre.ca/. [Accessed: April 4,2024].
[5]
" How we work, "Booking, 2023. [Online]. Available: https://www.booking.com/content/how_we_work.html?aid=397613&label=gog235jc-1FCAEoggI46AdIM1gDaAKIAQGYAQG4ARfIAQzYAQHoAQH4AQ2IAgGoAgO4Aof57o4GwAIB0gIkYWExM2EyYjQtNjgzNy00MDFhLWFlMmMtMmI0ODVlNTg0MDM02AIG4AIB&sid=05784999ca7f524cd4a9663b201f4889 . [Accessed: April 4,2024].
[6]
"The Experience of Travel: Why travel is so much more than a trip, "Tripadvisor, 2023. [Online]. Available: https://www.tripadvisor.com/business/insights/uncategorized/resources/the-experience-of-travel-why-travel-is-so-much-more-than-a-trip . [Accessed: April 4,2024].
[7]
A. Menk, L. Sebastia, and R. Ferreira, "Recommendation Systems for Tourism Based on Social Networks: A Survey," Mar. 2019, [Online]. Available: http://arxiv.org/abs/1903.12099. [Accessed: April 1,2024].
[8]
Merriam-Webster Dictionary, "Social Media | Definition of Social Media by Merriam-Webster," 31 May 2018. 2018.
[9]
K. A. Vakeel and S. Ray, "A motivation-aware approach for point of interest recommendations," in CEUR Workshop Proceedings, 2016, vol. 1685.
[10]
L. Cao, J. Luo, A. Gallagher, X. Jin, J. Han, and T. S. Huang, "A WORLDWIDE TOURISM RECOMMENDATION SYSTEM BASED ON GEOTAGGED WEB PHOTOS Kodak Research Laboratories, Eastman Kodak Company Dept. Computer Science, University of Illinois at Urbana-Champaign," Icassp, pp. 2274–2277, 2010.
[11]
K. Meehan, T. Lunney, K. Curran, and A. McCaughey, "Context-aware intelligent recommendation system for tourism," 2013 IEEE Int. Conf. Pervasive Comput. Commun. Work. PerCom Work. 2013, no. March, pp. 328–331, 2013.
[12]
E. Asani, H. Vahdat-nejad, and J. Sadri, "Restaurant recommender system based on sentiment analysis," Mach. Learn. with Appl., vol. 6, no. July, p. 100114, 2021.
[13]
X. Zou, M. Gonzales, and S. Saeedi, "A Context-Aware Recommendation System Using Smartphone Sensors," 2016.
[14]
B. Koster, I. I. Manager, and M. Van Gaalen, “Social Media Update 2016,” Pew Res. Cent., vol. 1, no. November, pp. 1–16, 2016, [Online]. Available: http://assets.pewresearch.org/wp-content/uploads/sites/14/2016/11/10132827/PI_2016.11.11_Social-Media-Update_FINAL.pdf. [Accessed: March 29,2024].
[15]
J. Gantz and D. Reinsel, "THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digi tal Shadows, and Biggest Growth in the Far East," Idc, vol. 2007, no. December 2012, pp. 1–16, 2012.
[16]
A. Balakrishnan and J. Boorstin, "Instagram says it now has 800 million users, up 100 million since April, "CNBC, 2017. [Online]. Available: https://www.cnbc.com/2017/09/25/how-many-users-does-instagram-have-now-800-million.html . [Accessed: April 4,2024].
[17]
"40 of social media users share photos of food they never eat," Ingenia, 2021. [Online]. Available: https://ingenia.com/news/2021/10/25/40-of-social-media-users-share-photos-of-food-they-never-eat/. [Accessed: April 4,2024].
[18]
A. M. Rashid, "Getting to Know You: Learning New User Preferences in Recommender Systems," no. February 2002.
[19]
J. Ben Schafer, J. Konstan, and J. Riedl, "Recommender Systems in E-Commerce," no. October 1999, doi 10.1145/336992.337035.
[20]
A. Valdivia, M. V. Luzón, and F. Herrera, “Sentiment Analysis in TripAdvisor,” IEEE Comput. Soc., 2017.
[21]
V. Bellotti, "Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide," pp. 1157–1166, 2008.
[22]
A. Rikitianskii, M. Harvey, and F. Crestani, "A Personalized Recommendation System for Context-Aware Suggestions."
[23]
N. Koubai and F. M. hame. Bouyakoub, "Myrestaurant: A smart restaurant with a recommendation system," Int. J. Comput. Digit. Syst., vol. 8, no. 2, pp. 143–156, 2019.
[24]
C. H. Chu and S. H. Wu, "A Chinese restaurant recommendation system based on mobile context-aware services," Proc. - IEEE Int. Conf. Mob. Data Manag., vol. 2, pp. 116–118, 2013.
[25]
I. Guy, N. Zwerdling, I. Ronen, D. Carmel, and E. Uziel, “Social Media Recommendation based on People and Tags,” no. Lc, pp. 194–201, 2010.
[26]
A. Majid, L. Chen, G. Chen, H. T. Mirza, I. Hussain, and J. Woodward, "A context-aware personalized travel recommendation system based on geotagged social media data mining," no. October 2014, pp. 37–41, 2012.
[27]
H. Feng and X. Qian, "Recommendation via user's personality and social contextual," Int. Conf. Inf. Knowl. Manag. Proc., no. October 2013, pp. 1521–1524, 2013.
[28]
E. Ortiz-Ospina, "The rise of social media," Our world in data, 2019. [Online]. Available: https://ourworldindata.org/rise-of-social-media. [Accessed: April 4,2024].
[29]
K. Das and S. K. Sinha, "A Survey on User Behaviour Analysis in Social Networks," Int. J. Comput. Sci. Inf. Secur., vol. 14, no. 11, pp. 895–908, 2016,[Online].Available:https://search.proquest.com/docview/1879098749?accountid=14874%0Ahttp://whel-primo.hosted.exlibrisgroup.com/openurl/44WHELF_BANG/44WHELF_BANG_services_page?genre=article&issn=&title=A+Survey+on+User+Behaviour+Analysis+in+Social+Networks&volume=14&issue=11. [Accessed: April 4,2024].
[30]
B. Vargas-Govea, G. González-Serna, and R. Ponce-Medellín, ‘Effects of relevant contextual features in the performance of a restaurant recommender system’, CEUR Workshop Proc., vol. 791, no. September, 2011.
[31]
W. Y. Zhang and G. M. Qian, ‘A new framework of a personalized location-based restaurant recommendation system in mobile application’, Int. Conf. Manag. Sci. Eng. - Annu. Conf. Proc., pp. 166–172, 2013.
[32]
X. Ramirez-Garcia and M. García-Valdez, ‘Post-filtering for a restaurant context-aware recommender system’, Stud. Comput. Intell., vol. 547, 2014.
[33]
K. A. Achmad, L. E. Nugroho, A. Djunaedi, and Widyawan, ‘Context-aware based restaurant recommender system: A prescriptive analytics’, J. Eng. Sci. Technol., vol. 14, no. 5, pp. 2847–2864, 2019.
[34]
M. Van Setten, S. Pokraev, and J. Koolwaaij, ‘Context-aware recommendations in the mobile tourist application COMPASS’, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3137, no. August 2004, pp. 235–244, 2004.
[35]
K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura, ‘Context-aware SVM for context-dependent information recommendation’, Proc. - IEEE Int. Conf. Mob. Data Manag., vol. 2006, no. April 2016, p. 109, 2006.
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- Intelligent Food Recommendation Framework Based on Social Media Behavioral Data
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Published In
May 2024
367 pages
ISBN:9798400716928
DOI:10.1145/3660853
Copyright © 2024 ACM.
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Association for Computing Machinery
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Published: 23 June 2024
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Presentation slides detailing an innovative Intelligent Food Recommendation Framework leveraging Social Media Behavioral Data. The framework employs advanced algorithms to analyze user interactions on social platforms, offering personalized food recommendations. Exploring the intersection of AI and social data, this research enhances culinary experiences by providing tailored suggestions, promising improved user engagement and satisfaction in food consumption. https://dl.acm.org/doi/10.1145/3660853.3660883#Intelligent Food Recommendation Framework Based on Social Media Behavioral Data_AICCONF24.pptx
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AICCONF '24
AICCONF '24: Cognitive Models and Artificial Intelligence Conference
May 25 - 26, 2024
İstanbul, Turkiye
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