Collaborative Filtering Based Food Recommendation System Using Matrix Factorization

 (*)Muhammad Bayu Samudra Siddik Mail (Telkom University, Bandung, Indonesia)
 Agung Toto Wibowo (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

Submitted: April 1, 2023; Published: July 23, 2023

Abstract

A recommendation system is a method that provides suggestions of items that might users like. There are many domains that can be recommended, one of the most demanded domains by users today is food. In the era of big data, food choices from the large amount of data make it difficult for users to choose the right food for them. The collaborative filtering (CF) approach is considered capable of providing accurate and high quality item suggestions. One of the algorithms that can provide good performance results from the CF approach is Matrix Factorization (MF). This study aims to test a dataset that contains product ratings of food items using three MF algorithms, which are Singular Value Decomposition (SVD), SVD with Implicit Ratings (SVD++), and Non-Negative Matrix Factorization (NMF). Different latent factors are also used for the purpose of improving the performance of the proposed recommendation system algorithm. The dataset used is Amazon Fine Food Reviews. The study shows NMF and SVD++ as the best algorithm for generating user rating predictions for items. NMF has the smallest average prediction error as measured by MAE which is 0.7311. While SVD++ obtains the smallest prediction error value of 1.0607 as measured using RMSE. In addition to these results, the top-n evaluation also shows that the proposed algorithm performs quite well. The hit ratio value for each different n-item always increases proportionally to the number of recommended n-items. The highest hit ratio value is generated from the SVD++ algorithm of 0.0025 on n-item recommendations of 25 items. Overall it can be said that the proposed algorithm has good performance in providing item recommendations.

Keywords


Food Recommender System; Collaborative Filtering; Matrix Factorization; MAE; RMSE

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References

K. Haruna et al., “Context-aware recommender system: A review of recent developmental process and future research direction,” Applied Sciences, vol. 7, no. 12, p. 1211, 2017.

F. Ricci, L. Rokach, and B. Shapira, “Recommender systems: introduction and challenges,” Recommender systems handbook, pp. 1–34, 2015.

M. B. Vivek, N. Manju, and M. B. Vijay, “Machine learning based food recipe recommendation system,” in Proceedings of International Conference on Cognition and Recognition: ICCR 2016, Springer, 2018, pp. 11–19.

W. Wang, L.-Y. Duan, H. Jiang, P. Jing, X. Song, and L. Nie, “Market2Dish: health-aware food recommendation,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 17, no. 1, pp. 1–19, 2021.

A. Al-Ansi, H. G. T. Olya, and H. Han, “Effect of general risk on trust, satisfaction, and recommendation intention for halal food,” Int J Hosp Manag, vol. 83, pp. 210–219, 2019.

Y. Afoudi, M. Lazaar, and M. Al Achhab, “Collaborative filtering recommender system,” in Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) Volume 5: Advanced Intelligent Systems for Computing Sciences, Springer, 2019, pp. 332–345.

D. Bianchini, V. De Antonellis, N. De Franceschi, and M. Melchiori, “PREFer: A prescription-based food recommender system,” Comput Stand Interfaces, vol. 54, pp. 64–75, 2017.

R. Mehta and K. Rana, “A review on matrix factorization techniques in recommender systems,” in 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), IEEE, 2017, pp. 269–274.

X. Yuan, L. Han, S. Qian, G. Xu, and H. Yan, “Singular value decomposition based recommendation using imputed data,” Knowl Based Syst, vol. 163, pp. 485–494, 2019.

J. Jiao, X. Zhang, F. Li, and Y. Wang, “A novel learning rate function and its application on the SVD++ recommendation algorithm,” IEEE Access, vol. 8, pp. 14112–14122, 2019.

N. Ifada, D. R. M. Alim, and M. K. Sophan, “NMF-based DCG Optimization for Collaborative Ranking on Recommendation Systems,” in Proceedings of the 2019 2nd International Conference on Machine Learning and Machine Intelligence, 2019, pp. 7–11.

M. C. Keshava, S. Srinivasulu, P. N. Reddy, and B. D. Naik, “Machine learning model for movie recommendation system,” Int. J. Eng. Res. Tech, vol. 9, pp. 800–801, 2020.

X. Li et al., “Application of intelligent recommendation techniques for consumers’ food choices in restaurants,” Front Psychiatry, vol. 9, p. 415, 2018.

R. Ahuja, A. Solanki, and A. Nayyar, “Movie recommender system using k-means clustering and k-nearest neighbor,” in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, 2019, pp. 263–268.

F. Xue, X. He, X. Wang, J. Xu, K. Liu, and R. Hong, “Deep item-based collaborative filtering for top-n recommendation,” ACM Transactions on Information Systems (TOIS), vol. 37, no. 3, pp. 1–25, 2019.

R. Anand and J. Beel, “Auto-surprise: An automated recommender-system (autorecsys) library with tree of parzens estimator (tpe) optimization,” in Proceedings of the 14th ACM Conference on Recommender Systems, 2020, pp. 585–587.

D. T. Tran and J.-H. Huh, “New machine learning model based on the time factor for e-commerce recommendation systems,” J Supercomput, pp. 1–46, 2022.

N. Mustafa, A. O. Ibrahim, A. Ahmed, and A. Abdullah, “Collaborative filtering: Techniques and applications,” in 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), IEEE, 2017, pp. 1–6.

N. Rajabpour, A. Naserasadi, and M. Estilayee, “TFR: a tourist food recommender system based on collaborative filtering,” Int J Comput Appl, vol. 975, p. 8887, 2018.

C. Yu, Q. Tang, Z. Liu, B. Dong, and Z. Wei, “A recommender system for ordering platform based on an improved collaborative filtering algorithm,” in 2018 International Conference on Audio, Language and Image Processing (ICALIP), IEEE, 2018, pp. 298–302.

D. Massimo, M. Elahi, M. Ge, and F. Ricci, “Item contents good, user tags better: Empirical evaluation of a food recommender system,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 373–374.

N. Hug, “Surprise: A Python library for recommender systems,” J Open Source Softw, vol. 5, no. 52, p. 2174, 2020.

M. Jallouli, S. Lajmi, and I. Amous, “When contextual information meets recommender systems: extended SVD++ models,” International Journal of Computers and Applications, vol. 44, no. 4, pp. 349–356, 2022.

X. Guan, C.-T. Li, and Y. Guan, “Matrix factorization with rating completion: An enhanced SVD model for collaborative filtering recommender systems,” IEEE access, vol. 5, pp. 27668–27678, 2017.

M. Ge, M. Elahi, I. Fernaández-Tobías, F. Ricci, and D. Massimo, “Using tags and latent factors in a food recommender system,” in Proceedings of the 5th International Conference on Digital Health 2015, 2015, pp. 105–112.

J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C.-H. Lee, “On mean absolute error for deep neural network based vector-to-vector regression,” IEEE Signal Process Lett, vol. 27, pp. 1485–1489, 2020.

T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci Model Dev, vol. 15, no. 14, pp. 5481–5487, 2022.

X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proceedings of the 26th international conference on world wide web, 2017, pp. 173–182.

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