Hybrid-based Recommender System for Online Shopping: A Review Manuscript Received: 8 February 2023, Accepted: 21 February 2023, Published: 15 March 2023, ORCiD: 0000-0002-7190-0837, https://doi.org/10.33093/jetap.2023.5.1.3

Main Article Content

Ying Fei Lim
Su Cheng Haw
Kok Why Ng
Elham Abdulwahab Anaam


In the era of the digital revolution, online shopping has developed into a remarkably simple and economical option for consumers to make purchases securely and conveniently from their homes. In order for the online merchant to optimize their profit, the online shopping platform must always display a list of potential products that customers may purchase. The recommender system kicks in at this point to assist in finding products that customers would like and recommend a list of product recommendations that match the customer's preferences. This paper reviews the recommender system technology in detail by reviewing the classification technique. Other than that, the related works will be reviewed to understand how each technique works, the strengths and limitations, the datasets and evaluation metrics employed.

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Q. Zhang, J. Lu and Y. Jin, “Artificial Intelligence in Recommender Systems,” Complex & Intellig. Sys., vol. 7(1), pp. 439–457, 2021.

X. Hu, W. Zhu and Q. Li, “HCRS: A Hybrid Clothes Recommender System based on User Ratings and Product Features,” Int. Conf. on Management of e-Commerce and e-Government, pp. 270-274, 2014.

C. Obeid, L. Lahoud, H. el Khoury and P.A. Champin, “Ontology-based Recommender System in Higher Education”, Companion of the The Web Conf., pp. 1031–1034, 2018.

S. Shruthi and V. J. Gripsy, “An Effective Product Recommendation System for E-Commerce Website Using Hybrid Recommendation Systems,” Int. J. of Comp. Sci. & Comm., vol. 8(2), pp. 81-88, 2017.

H. Kaur and G. Bathla, “Techniques of Recommender System,” Int. J. of Innov. Techn. and Explor. Eng., vol. 8(9S), pp. 373–379, 2019.

F. O. Isinkaye, Y. O. Folajimi and B. A. Ojokoh, “Recommendation Systems: Principles, Methods and Evaluation,” Egyptian Inform. J., vol. 16(3), pp. 261–273, 2015.

C. Yang, X. Yu, Y. Liu, Y. Nie and Y. Wang, “Collaborative Filtering with Weighted Opinion Aspects”, Neurocomputing, vol. 210, pp. 185–196, 2019.

F. Ricci, L. Rokach, B. Shapira and P. B. Kantor, “Recommender Systems Handbook,” Springer US, 2019.

F. García-Sánchez, R. Colomo-Palacios and R. Valencia-García, “A Social-semantic Recommender System for Advertisements,” Inform. Process. & Manage., vol. 57(2), 102153, 2020.

J. L. Herlocker, J. A. Konstan, L. G. Terveen and J. T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. on Inform. Sys., vol. 22(1), pp. 5–53, 2004.

J. Li, K. Zhang, X. Yang, P. Wei, J. Wang, K. Mitra and R. Ranjan, “Category Preferred Canopy–K-means Based Collaborative Filtering Algorithm,” Future Generation Comp. Sys., vol. 93, pp. 1046–1054, 2019.

A. Patel, A. Thakkar, N. Bhatt and P. Prajapati, “Survey and Evolution Study Focusing Comparative Analysis and Future Research Direction in the Field of Recommendation System Specific to Collaborative Filtering Approach,” In Satapathy, S., Joshi, A. (eds) Inform. and Comm. Techn. for Intellig. Sys.. Smart Innov., Sys. and Techn., vol 106. Springer, 2019.

N. Mustafa, A.O. Ibrahim, A. Ahmed and A. Abdullah, “Collaborative Filtering: Techniques and Applications,” Int. Conf. on Comm., Control, Comput. and Electronics Eng., pp. 1–6, 2019.

P. B. Thorat, R. M. Goudar and S. Barve, “Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System,” Int. J. of Comput. Appl., vol. 110(4), pp. 31–36, 2019.

F. Ortega, R. Hurtado, J. Bobadilla and R. Bojorque, “Recommendation to Groups of Users Using the Singularities Concept,” IEEE Access, vol. 6, pp. 39745–39761, 2018.

S. M. Al-Ghuribi and S. A. M Noah, “A Comprehensive Overview of Recommender System and Sentiment Analysis,” Preprint, 2021. https://doi.org/10.48550/arXiv.2109.08794

H. P. Ambulgekar, M. K. Pathak and M. B. Kokare, “A Survey on Collaborative Filtering: Tasks, Approaches and Applications,” Int. Ethic. Hacking Conf. Advanc. in Intellig. Sys. and Comp., vol 811, 2018.

M. V. Kumar and P. N. V. S. Pavan Kumar, “A Study on Different Phases and Various Recommendation System Techniques,” Int. J. of Recent Techn. and Eng., vol. 7(5C), pp. 38-41, 2019.

M. Lagerstedt and M. Olsson, “A Hybrid Recommender System for Usage Within e-commerce,” Master Thesis, Chalmers University of Technology, Sweden, 2017.

F. T. A Hussien, A. M. S. Rahma and H. B. A. Wahab, “Recommendation Systems for E-commerce Systems An Overview,” J. of Phys.: Conf. Series, vol. 1897(1), 012024, pp. 1-14, 2021.

F. Hdioud, B. Frikh and B. Ouhbi, “Multi-Criteria Recommender Systems Based on Multi-Attribute Decision Making,” Int. Conf. on Inform. Integr. and Web-Based Appl. & Serv., pp. 203–210, 2013.

J. B. Schafer, D. Frankowski, J. Herlocker and S. Sen, “Collaborative Filtering Recommender Systems,” Lecture Notes in Comp. Sci., pp. 291–324, 2007.

G. Ali and A. Elkorany, “Semantic-based Collaborative Filtering for Enhancing Recommendation,” Int. Conf. on Knowledge Eng. and Ontol. Develop., pp. 176-185, 2014.

P. Sheridan, M. Onsjo, M., C. Becerra, S. Jimenez and G. Genas, “An Ontology-Based Recommender System With An Application to The Star Trek Television Franchise,” Future Internet, vol. 11(9), pp. 182, 2019.

T. R. Gruber, “A Translation Approach to Portable Ontology Specifications,” Knowledge Acquis., vol. 5(2), pp. 199–220, 1993.

S. E. Middleton, D. D. Roure and N. R. Shadbolt, "Ontology-Based Recommender Systems," Handbook on Ontologies, pp. 779–796, Springer, 2009.

X. Li and H. Chen, “Recommendation As Link Prediction in Bipartite Graphs: A Graph Kernel-based Machine Learning Approach,” Decis. Supp. Sys., vol. 54(2), pp. 880–890, 2013.

S. Kamta and V. Verma, “A Survey on Graph-Based Collaborative Filtering Techniques in Recommender Systems,” Int. J. of Knowledge Based Comp. Sys., vol. 7(2), 2019.

B. Shams and S. Haratizadeh, “Graph-based Collaborative Ranking,” Expert Sys. with Appl., vol. 67, pp. 59–70, 2017.

B. B. Sinha and R. Dhanalakshmi, “Evolution of Recommender Paradigm Optimization Over Time,” J. of King Saud Univ. – Comp. and Inform. Sci., vol. 34(4), pp. 1047–1059, 2022.

A. Adala, N. Tabbane and S. Tabbane, “A Framework for Automatic Web Service Discovery Based on Semantics and NLP Techniques,” Advanc. in Multimedia, vol. 2011, pp. 1-8, 2011.

A. Elgohary, H. Nomir, I. Sabek, M. Samir, M. Badawy and N.A. Yoursi, “Wiki-rec: A Semantic-based Recommendation System Using Wikipedia As An Ontology,” Int. Conf. on Intellig. Sys. Design and Appl., pp. 1465–1470, 2010.

C. Martinez-Cruz, C. Porcel, J. Bernabe-Moreno and E. Herrera-Viedma, “A Model to Represent Users Trust in Recommender Systems Using Ontologies and Fuzzy Linguistic Modeling,” Inform. Sci., vol. 311, pp. 102–118, 2015.

T. Badriyah, E. T. Wijayanto, I. Syarif and P. Kristalina, “A Hybrid Recommendation System for E-commerce Based on Product Description and User Profile,” Int. Conf. on Innov. Comput. Techn., pp. 95–100, 2017.

M. Nilashi, O. Ibrahim and K. Bagherifard, “A Recommender System Based on Collaborative Filtering Using Ontology and Dimensionality Reduction Techniques,” Expert Sys. with Appl., vol. 92, pp. 507–520, 2018.

R. A. El-Deen, S. Morsi and N. Magdi, “Using Semantic Web Technology and Data Mining for Personalized Recommender System to Online Shopping,” Int. Conf. on Comp. and Appl., pp. 358–363, 2018.

M. Guia, R. R. Silva and J. Bernardino, “A Hybrid Ontology-Based Recommendation System in e-Commerce,” Algorithms, vol. 12(11), pp. 239, 2019.

M. Nasir and C. I. Ezeife, “Semantics Embedded Sequential Recommendation for E-Commerce Products (SEMSRec),” Int. Conf. on Advanc. in Social Netw. Analy. and Mining, pp. 270–274, 2020.

M. Kartheek and G. P. Sajeev, “Building Semantic Based Recommender System Using Knowledge Graph Embedding,” Int. Conf. on Image Informa. Process., pp. 25–29, 2020.

H. Hanafi, “Enhance Rating Prediction for E-commerce Recommender System Using Hybridization of SDAE, Attention Mechanism and Probabilistic Matrix Factorization,” Int. J. of Intellig. Eng. and Sys., vol. 15(5), pp. 427–438, 2022.