Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

Enhancing Recommendation System using Ontology-based Similarity and Incremental SVD Prediction

Author(s): Sajida Mhammedi*, Noreddine Gherabi, Hakim El Massari and Mohamed Amnai

Volume 16, Issue 9, 2023

Published on: 25 September, 2023

Article ID: e230823220260 Pages: 10

DOI: 10.2174/2666255816666230823125227

Price: $65

Abstract

Background: With the explosion of data in recent years, recommender systems have become increasingly important for personalized services and enhancing user engagement in various industries, including e-commerce and entertainment. Collaborative filtering (CF) is a widely used approach for generating recommendations, but it has limitations in addressing issues such as sparsity, scalability, and prediction errors.

Methods: To address these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines an incremental singular value decomposition approach with an item-based ontological semantic filtering approach in both online and offline phases. The ontologybased technique improves the accuracy of predictions and recommendations. The proposed method is evaluated on a real-world movie recommendation dataset using several performance metrics, including precision, F1 scores, and MAE.

Results: The results demonstrate that the proposed method outperforms existing methods in terms of accuracy while also addressing sparsity and scalability issues in recommender systems. Additionally, our approach has the advantage of reduced running time, making it a promising solution for practical applications.

Conclusion: The proposed method offers a promising solution to the challenges faced by traditional CF methods in recommender systems. By combining incremental SVD and ontological semantic filtering, the proposed method not only improves the accuracy of predictions and recommendations but also addresses issues related to scalability and sparsity. Overall, the proposed method has the potential to contribute to the development of more accurate and efficient recommendation systems in various industries, including e-commerce and entertainment.

Graphical Abstract

[1]
F.O. Isinkaye, Y.O. Folajimi, and B.A. Ojokoh, "Recommendation systems: Principles, methods and evaluation", Egypt. Inform. J., vol. 16, no. 3, pp. 261-273, 2015.
[http://dx.doi.org/10.1016/j.eij.2015.06.005]
[2]
H. Khojamli, and J. Razmara, "Survey of similarity functions on neighborhood-based collaborative filtering", Expert Syst. Appl., vol. 185, p. 115482, 2021.
[http://dx.doi.org/10.1016/j.eswa.2021.115482]
[3]
S. Kumar Addagarla, "A survey on comprehensive trends in recommendation systems & applications", Int. J. Electron. Commer. Stud., vol. 10, no. 1, pp. 65-88, 2019.
[http://dx.doi.org/10.7903/ijecs.1705]
[4]
Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, "Recommendation systems: Algorithms, challenges, metrics, and business opportunities", Appl. Sci., vol. 10, no. 21, p. 21, 2020.
[http://dx.doi.org/10.3390/app10217748]
[5]
S. Middleton, "Capturing knowledge of user preferences with recommender systems", Available From: https://www.semanticscholar.org/paper/Capturing-knowledge-of-user-preferences-with-Middleton/8d6299987d84e07c693398b51f0e88f17d244b10
[6]
H. El Massari, N. Gherabi, S. Mhammedi, H. Ghandi, M. Bahaj, and M.R. Naqvi, "The impact of ontology on the prediction of cardiovascular disease compared to machine learning algorithms", Int. J. Online Biomed. Eng., vol. 18, no. 11, p. 11, 2022.
[http://dx.doi.org/10.3991/ijoe.v18i11.32647]
[7]
M. Arafeh, P. Ceravolo, A. Mourad, E. Damiani, and E. Bellini, "Ontology based recommender system using social network data", Future Gener. Comput. Syst., vol. 115, pp. 769-779, 2021.
[http://dx.doi.org/10.1016/j.future.2020.09.030] [PMID: 33071400]
[8]
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Incremental singular value decomposition algorithms for highly scalable recommender systems", Available From: https://www.ics.uci.edu/~djp3/classes/2007_04_02_CS221/Lecture19/Incremental%20Singular%20Value%20Decomposition%20Algorithms%20for%20Highly%20Scalable.pdf
[9]
F.J. Igo, M. Brand, K. Wittenburg, D.W.H. Wong, and S. Azuma, "Multidimensional visualization for collaborative filtering recommender systems", Available From: https://www.semanticscholar.org/paper/Multidimensional-Visualization-for-Collaborative-Igo-Brand/b52e3458b2f65919fb04b2e98cd699a6a02df60d
[10]
M. Nilashi, O. Ibrahim, and K. Bagherifard, "A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques", Expert Syst. Appl., vol. 92, pp. 507-520, 2018.
[http://dx.doi.org/10.1016/j.eswa.2017.09.058]
[11]
J. Wang, P. Han, Y. Miao, and F. Zhang, "A Collaborative Filtering Algorithm Based on SVD and Trust Factor", 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019), Atlantis, 2019, pp. 33-39.
[12]
N. Bhalse, and R. Thakur, "Withdrawn: Algorithm for movie recommendation system using collaborative filtering", Mater. Today Proc., 2021.
[http://dx.doi.org/10.1016/j.matpr.2021.01.235]
[13]
T. Anwar, V. Uma, and G. Srivastava, "Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++", Int. J. Inf. Technol. Decis. Mak, vol. 20, no. 04, pp. 1075-1093, 2021.
[http://dx.doi.org/10.1142/S0219622021500310]
[14]
T. Anwar, V. Uma, and G. Srivastava, "CDRec-CAS: Cross-domain recommendation using context-aware sequences", IEEE Trans. Comput. Soc. Syst., pp. 1-10, 2023.
[http://dx.doi.org/10.1109/TCSS.2022.3233781]
[15]
T. Anwar, and V. Uma, "CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining", J. King Saud Univ. Comput. Inf. Sci., vol. 34, no. 3, pp. 793-800, 2022.
[http://dx.doi.org/10.1016/j.jksuci.2019.01.012]
[16]
S. Mhammedi, H. El Massari, N. Gherabi, and M. Amnai, "CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF).", In: Artificial Intelligence and Smart Environment., vol. 635. Springer: Heidelberg, 2023, pp. 313-318.
[17]
Institut Teknologi Sepuluh Nopember, "Semantic Recommender System Based on Semantic Similarity Using FastText and Word Mover’s Distance", Int. J. Intell. Eng. Syst., vol. 14, no. 2, pp. 377-385, 2021.
[http://dx.doi.org/10.22266/ijies2021.0430.34]
[18]
S. Mhammedi, H. El Massari, and N. Gherabi, "Composition of Large Modular Ontologies Based on Structure", In: Advances in Information, Communication and Cybersecurity., Springer: Heidelberg, 2021, pp. 144-154.
[19]
R-Q. Wang, and F-S. Kong, "Semantic-Enhanced Personalized Recommender System", In 2007 International Conference on Machine Learning and Cybernetics, Hong Kong, China, 2007, pp. 4069-4079
[20]
S. Bag, S.K. Kumar, and M.K. Tiwari, "An efficient recommendation generation using relevant Jaccard similarity", Inf. Sci., vol. 483, pp. 53-64, 2019.
[http://dx.doi.org/10.1016/j.ins.2019.01.023]
[21]
A. Daoui, N. Gherabi, and A. Marzouk, "A new approach for measuring semantic similarity of ontology concepts using dynamic programming", arXiv:1904.08501, 2005.
[22]
R. Katarya, and O.P. Verma, "An effective web page recommender system with fuzzy c-mean clustering", Multimedia Tools Appl., vol. 76, no. 20, pp. 21481-21496, 2017.
[http://dx.doi.org/10.1007/s11042-016-4078-7]
[23]
N. Bassiliades, M. Symeonidis, G. Meditskos, E. Kontopoulos, P. Gouvas, and I. Vlahavas, "A semantic recommendation algorithm for the PaaSport platform-as-a-service marketplace", Expert Syst. Appl., vol. 67, pp. 203-227, 2017.
[http://dx.doi.org/10.1016/j.eswa.2016.09.032]
[24]
T. Ma, "Social Network and Tag Sources Based Augmenting Collaborative Recommender System", IEICE Transactions on Information and Syst.ems, vol. 10, pp. 902-910, 2015.
[25]
X. Ning, C. Desrosiers, and G. Karypis, "A Comprehensive Survey of Neighborhood-Based Recommendation Methods", In: F. Ricci, L. Rokach, and B. Shapira, EdsRecommender Systems Handbook. Springer US: Boston, MA, 2015, pp. 37-76.

Rights & Permissions Print Cite
© 2025 Bentham Science Publishers | Privacy Policy