1. MMC
  2. All Volumes and Issues
  3. Volume 11, Number 4, 2024
  4. Recommendation systems techniques based on generative models and matrix factorization: a survey

Recommendation systems techniques based on generative models and matrix factorization: a survey

MMC.
2024;
Volume 11, Number 4
: pp. 1078–1092
Received: January 20, 2024
Revised: August 19, 2024
Accepted: August 22, 2024

Filali Zegzouti S., Banouar O., Benslimane M.  Recommendation systems techniques based on generative models and matrix factorization: a survey.  Mathematical Modeling and Computing. Vol. 11, No. 4, pp. 1078–1092 (2024)

Authors:
  1. S. Filali Zegzouti
  2. O. Banouar
  3. M. Benslimane
1
Sciences, Engineering and Management Laboratory, Sidi Mohamed Ben Abdellah University, Fez
2
Laboratory of Computer and Systems Engineering, Cadi Ayyad University, Marrakesh
3
Sciences, Engineering and Management Laboratory, Sidi Mohamed Ben Abdellah University, Fez

Collaborative filtering (CF) is a technique that can filter out items that a user might like based on the behaviors and preferences of similar users.  It is a key en-abler technique for an effective recommendation system (RS).  Model-based recommendation systems, a subset of CF, use data, typically ratings, to construct models for providing personalized suggestions to users.  Our objective in this work is to provide a comprehensive overview of various techniques employed in Model-based RS, focusing on their theoretical foundations and practical applications.  We explore the core challenges associated with recommendation, including the top-N recommendation problem, and explore the state-of-the-art model-based methods used to address these challenges.  In this survey, we categorize these techniques into three distinct classes: matrix factorization, similarity-based, and completion-based methods.  To compare their performance, we evaluated these techniques over the MovieLens datasets using two metrics: Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), precision and recall.

recommendation systems
model-based
top-N recommendation
generative adversarial network
matrix factorization
matrix completion
  1. Momoh F. O., Rakshit S., Vajjhala N. R.  Exploratory study of machine learning algorithms in recommender systems.  Proceedings of International Conference on Advanced Computing Applications.  571–580 (2021).
  2. Sharma R., Singh R.  Evolution of recommender systems from ancient times to modern era: a survey.  Indian Journal of Science and Technology.  9 (20), 1–12 (2016).
  3. Li S. S., Karahanna E.  Online recommendation systems in a B2C E-commerce context: a review and future directions.  Journal of the Association for Information Systems.  16 (2), 72–107 (2015).
  4. Beel J., Langer S., Nürnberger A., Genzmehr M.  The Impact of Demographics (Age and Gender) and Other User-Characteristics on Evaluating Recommender Systems.  Research and Advanced Technology for Digital Libraries. 396–400 (2013).
  5. Bhuvaneshwari P., Rao A. N., Robinson Y. H.  Top-N Recommendation System Using Explicit Feedback and Outer Product Based Residual CNN.  Wireless Personal Communications.  128, 967–983 (2023).
  6. Filali-Zegzouti S., Banouar O., Benslimane M.  Classification of Recommender systems using Deep Learning based generative models. Proceedings of the Statistics and Data Science Conference.  164–169 (2023).
  7. Van Meteren R., Van Someren M.  Using content-based filtering for recommendation.  Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop.  30, 47–56 (2000).
  8. Afoudi Y., Lazaar M., Al Achhab M.  Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network.  Simulation Modelling Practice and Theory.  113, 102375 (2021).
  9. Papadakis H., Papagrigoriou A., Panagiotakis C., Kosmas E., Fragopoulou P.  Collaborative filtering recommender systems taxonomy.  Knowledge and Information Systems.  64, 35–74 (2022).
  10. Sarik G., Nematbakhsh M. A.  Enhancing memory-based collaborative filtering for group recommender systems.  Expert Systems with Applications.  42 (7), 3801–3812 (2015).
  11. Aditya P. H., Budi I., Munajat Q.  A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: A case study PT X.  2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS).  303–308 (2016).
  12. Movafegh Z., Rezapour A.  Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition.  Engineering Applications of Artificial Intelligence.  126 (D), 107109 (2023).
  13. Ma Y., Liu Q.  Generalized matrix factorization based on weighted hypergraph learning for microbe-drug association prediction.  Computers in Biology and Medicine.  145, 105503 (2022).
  14. Tran T., Lee K., Liao Y., Lee D.  Regularizing matrix factorization with user and item embeddings for recommendation.  Proceedings of the 27th ACM international conference on information and knowledge management.  687–696 (2018).
  15. Dervishaj E., Cremonesi P.  GAN-based matrix factorization for recommender systems.  Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 1373–1381 (2022).
  16. Ouedghiri O., Banouar O., Raghay S., El Haddaj S.  Intelligent recommender system based on quantum clustering and matrix completion.  Concurrency Computation Practice and Experience.  34 (15), e6943 (2022).
  17. Cai J.-E., Candès E. J., Zuowei S.  A singular value thresholding algorithm for matrix completion.  SIAM Journal on Optimization.  20 (4), 1956–1982 (2010).
  18. Christakopoulou E.  Improving the Quality of Top-N Recommendation.  University of Minnesota Ph.D. dissertation (2018).
  19. Anamisa D. R., Jauhari A., Mufarroha F. A.  K-Nearest Neighbors Method for Recommendation System in Bangkalan's Tourism.  ComTech: Computer, Mathematics and Engineering Applications.  14 (1), 33–44 (2023).
  20. Cooper C., Lee S. H., Radzik T., Siantos Y.  Random walks in recommender systems: exact computation and simulations.  Proceedings of the 23rd International Conference on World Wide Web.  811–816 (2014).
  21. Chae D.-K., Kang J.-S., Kim S.-W., Lee J.-T.  CFGAN: A generic collaborative filtering framework based on generative adversarial networks.  Proceedings of the 27th ACM International Conference on Information and Knowledge Management.  137–146 (2018).
  22. Chae D.-K., Shin J. A., Kim S.-W.  Collaborative adversarial autoencoders: An effective collaborative filtering model under the GAN framework.  IEEE Access.  7, 37650–37663 (2019).
  23. Ghazarian S., Nematbakhsh M. A.  Enhancing memory-based collaborative filtering for group recommender systems.  Expert Systems with Applications.  42 (7), 3801–3812 (2014).
  24. Wang Q., Ji Z., Liu H., Zhao B.  Deep Bayesian Multi-Target Learning for Recommender Systems.  Preprint arXiv:1902.09154v1 (2019).
  25. Saberi D. M., Spina S. R.  Deep Bayesian Recommendation Systems (2018).
  26. Rahman S.  Extended Collaborative Filtering Recommendation System with Adaptive KNN and SVD.  International Journal of Engineering and Management Research.  13 (4), 105–112 (2023).
  27. Kumar R., Verma B. K., Rastogi S. S.  Social popularity based SVD++ recommender system.  International Journal of Computer Applications.  87 (14), 33–37 (2014).
  28. Huang H.  Enhancing Recommender Systems with Causal Inference Methodologies.  UWSpace (2023).
  29. Lee D. D., Seung H. S.  Learning the parts of objects by non-negative matrix factorization.  Nature.  401 (6755), 788–791 (1999).
  30. Adyatma H. A., Baizal Z. K. A.  Book Recommender System Using Matrix Factorization with Alternating Least Square Method.  Journal of Information System Research.  4 (4), 1286–1292 (2023).
  31. Jannani A., Sael N., Benabbou F.  Machine learning for the analysis of quality of life using the World Happiness Index and Human Development Indicators.  Mathematical Modeling and Computing.  10 (2), 534–546 (2023).
  32. Tian B., Gu Y., Liu S.  A Probability Matrix Factorization for User Behavior Perception Recommendation Model.  2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA).  143–146 (2023).
  33. Zhang Z., Wu Q., Zhang Y., Liu L.  Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration.  PeerJ Computer Science.  9, e1338 (2023).
  34. Liu N., Zhao J.  Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization.  IEEE Access.  11, 16994–17001 (2023).
  35. Kuchma M. I., Gatalevych A. I.  Triangular form of Laurent polynomial matrices and their factorization.  Mathematical Modeling and Computing.  9 (1), 119–129 (2022).
  36. Goodfellow I. J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y.  Generative adversarial nets.  Proceedings of the 27th International Conference on Neural Information Processing Systems.  2, 2672–2680 (2014).
  37. Qin W., Wu H., Lai Q., Wang C.  A Parallelized, Momentum-incorporated Stochastic Gradient Descent Scheme for Latent Factor Analysis on High-dimensional and Sparse Ma-trices from Recommender Systems.  2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).  1744–1749 (2019).
  38. Creswell A., White T., Dumoulin V., Arulkumaran K., Sengupta B., Bharath A. A.  Generative adversarial networks: An overview.  IEEE Signal Processing Magazine.  35 (1), 53–65 (2018).
  39. Ning X., Karypis G.  SLIM: Sparse Linear Methods for Top-N Recommender Systems.  2011 IEEE 11th International Conference on Data Mining. 497–506 (2011).
  40. Wang H., Wang J., Wang J., Zhao M., Zhang W., Zhang F., Li W., Xie X., Guo M.  Learning graph representation with generative adversarial nets.  IEEE Transactions on Knowledge and Data Engineering.  33 (8), 3090–3103 (2019).
  41. Deshpande M., Karypis G.  Item-based top-$N$ recommendation algorithms.  ACM Transactions on Information Systems (TOIS).  22 (1), 143–177 (2004).
  42. Mohaoui S., El Qate K., Hakim A., Raghay S.  Low-rank tensor completion using nonconvex total variation.  Mathematical Modeling and Computing.  9 (2), 365–374 (2022).
  43. Banouar O., Raghay S.  Enriching SPARQL Queries by User Preferences for Results Adaptation.  International Journal of Software Engineering and Knowledge Engineering.  28 (08), 1195–1221 (2018).