References
- 1. Patro, S. G. K., B. K. Mishra, S. K. Panda, R. Kumar, A. Apoorva. Hybrid Social Recommender Systems for Electronic Commerce: A Review. – In: 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA’20), IEEE, 2020, pp. 1-6.
- 2. Li, Y. M., C. T. Wu, C. Y. Lai. A Social Recommender Mechanism for e-Commerce: Combining Similarity, Trust, and Relationship. – Decision Support Systems, Vol. 55, 2013, No 3, pp. 740-752.10.1016/j.dss.2013.02.009
- 3. Mohana, H., M. Suriakala. An Enhanced Prospective Jaccard Similarity Measure (PJSM) to Calculate the User Similarity Score Set for e-Commerce Recommender System. – In: S. Satapathy, V. Bhateja, B. Janakiramaiah, Y. W. Chen, Eds. Advances in Intelligent Systems and Computing. Singapore, Springer, 2021, pp. 129-142.10.1007/978-981-15-5400-1_14
- 4. Schafer, J. B., J. Konstan, J. Riedl. Recommender Systems in e-Commerce. – In: Proc. of 1st ACM Conference on Electronic Commerce, ACM, 1999, pp. 158-166.10.1145/336992.337035
- 5. Shambour, Q., J. Lu. An Effective Recommender System by Unifying User and Item Trust Information for B2B Applications. – Journal of Computer and System Sciences, Vol. 81, 2015, No 7, pp. 1110-1126.10.1016/j.jcss.2014.12.029
- 6. Aggarwal, C. C. Neighborhood-Based Collaborative Filtering. – In: Recommender Systems: The Textbook, Springer International Publishing, Cham, 2016, pp. 29-70.10.1007/978-3-319-29659-3_2
- 7. Ambulgekar, H. P., M. K. Pathak, M. B. Kokare. A Survey on Collaborative Filtering: Tasks, Approaches and Applications. – In: M. Chakraborty, S. Chakrabarti, V. Balas, J. Mandal, Ed. Advances in Intelligent Systems and Computing, Singapore, Springer, 2019, pp. 289-300.
- 8. Shambour, Q. Y., M. M. Abu-Alhaj, M. M. Al-Tahrawi. A Hybrid Collaborative Filtering Recommendation Algorithm for Requirements Elicitation. – International Journal of Computer Applications in Technology, Vol. 63, 2020, No 1-2, pp. 135-146.10.1504/IJCAT.2020.107908
- 9. Schafer, J. B., D. Frankowski, J. Herlocker, S. Sen. Collaborative Filtering Recommender Systems. – In: P. Brusilovsky, A. Kobsa W. Nejdl, Ed. The Adaptive Web: Methods and Strategies of Web Personalization, Berlin, Heidelberg, Springer, 2007, pp. 291-324.
- 10. Adomavicius, G., A. Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. – IEEE Transactions on Knowledge and Data Engineering, Vol. 17, 2005, No 6, pp.734-749.10.1109/TKDE.2005.99
- 11. Shambour, Q. A User-Based Multi-Criteria Recommendation Approach for Personalized Recommendations. – International Journal of Computer Science and Information Security, Vol. 14, 2016, No 12, pp. 657-663.
- 12. Shambour, Q., M. Hourani, S. Fraihat. An Item-Based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems. – International Journal of Advanced Computer Science and Applications, Vol. 7, 2016, No 8, pp. 274-279.10.14569/IJACSA.2016.070837
- 13. Putra, A. A., R. Mahendra, I. Budi, Q. Munajat. Two-Steps Graph-Based Collaborative Filtering Using User and Item Similarities: Case Study of e-Commerce Recommender Systems. – In: Proc. of 2017 International Conference on Data and Software Engineering (ICoDSE’17), IEEE, 2017, pp. 1-6.
- 14. Chu, P., S. Lee. A Novel Recommender System for e-Commerce. – In: Proc. of 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI’17), IEEE, 2017, pp. 1-5.10.1109/CISP-BMEI.2017.8302310
- 15. Xiao, Y., C. I. Ezeife. e-Commerce Product Recommendation Using Historical Purchases and Clickstream Data. – In: C. Ordonez, L. Bellatreche, Eds. Big Data Analytics and Knowledge Discovery. Cham, Springer International Publishing, 2018, pp. 70-82.10.1007/978-3-319-98539-8_6
- 16. Choi, Y. K., S. K. Kim. A Recommendation System for Repetitively Purchasing Items in e-Commerce Based on Collaborative Filtering and Association Rules. – Journal of Internet Technology, Vol. 19, 2018, No 6, pp.1691-1698.
- 17. Sassani, B., A. Alahmadi, H. Sharifzadeh. A Cluster Based Collaborative Filtering Method for Improving the Performance of Recommender Systems in e-Commerce. – In: K. Arai, R. Bhatia, S. Kapoor, Eds. Advances in Intelligent Systems and Computing. Cham, Springer International Publishing, 2019, pp. 990-1001.10.1007/978-3-030-02683-7_73
- 18. Khodabandehlou, S. Designing an e-Commerce Recommender System Based on Collaborative Filtering Using a Data Mining Approach. – International Journal of Business Information Systems, Vol. 31, 2019, No 4, pp. 455-478.10.1504/IJBIS.2019.10023056
- 19. Jiang, L., Y. Cheng, L. Yang, J. Li, H. Yan, X. Wang. A Trust-Based Collaborative Filtering Algorithm for e-Commerce Recommendation System. – Journal of Ambient Intelligence and Humanized Computing, Vol. 10, 2019, No 8, pp. 3023-3034.10.1007/s12652-018-0928-7
- 20. Iftikhar, A., M. A. Ghazanfar, M. Ayub, Z. Mehmood, M. Maqsood. An Improved Product Recommendation Method for Collaborative Filtering. – IEEE Access, Vol. 8, 2020, pp. 123841-123857.10.1109/ACCESS.2020.3005953
- 21. Singh, M. K., O. P. Rishi. Event Driven Recommendation System for e-Commerce Using Knowledge Based Collaborative Filtering Technique. – Scalable Computing: Practice and Experience, Vol. 21, 2020, No 3, pp. 369-378.10.12694/scpe.v21i3.1709
- 22. Dyer, J. S. MAUT – Multiattribute Utility Theory. – In: Multiple Criteria Decision Analysis: State of the Art Surveys. New York, Springer, 2005, pp. 265-292.10.1007/0-387-23081-5_7
- 23. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. – In: Proc. of 1994 ACM Conference on Computer Supported Cooperative Work, ACM, 1994, pp. 175-186.
- 24. Gazdar, A., L. Hidri. A New Similarity Measure for Collaborative Filtering Based Recommender Systems. – Knowledge-Based Systems, Vol. 188, 2020, pp.58-105.10.1016/j.knosys.2019.105058
- 25. Barzegar Nozari, R., H. Koohi, E. Mahmodi. A Novel Trust Computation Method Based on User Ratings to Improve the Recommendation. – International Journal of Engineering, Vol. 33, 2020, No 3, pp.377-386.10.5829/ije.2020.33.03c.02
- 26. Papagelis, M., D. Plexousakis, T. Kutsuras. Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences. – In: P. Herrmann, V. Issarny, S. Shiu, Ed. iTrust 2005: Trust Management. Berlin, Heidelberg, Springer, 2005, pp. 224-239.
- 27. Frakes, W. B., R. Baeza-Yates. Information Retrieval: Data Structures and Algorithms. Prentice Hall, 1992.
- 28. Herlocker, J., J. A. Konstan, J. Riedl. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. – Information Retrieval, Vol. 5, 2002, No 4, pp. 287-310.10.1023/A:1020443909834
- 29. Burke, R. Hybrid Web Recommender Systems. – In: P. Brusilovsky, A. Kobsa W. Nejdl, Ed. The Adaptive Web: Methods and Strategies of Web Personalization. Berlin, Heidelberg, Springer, 2007, pp. 377-408.
- 30. O’Donovan, J., B. Smyth. Trust in Recommender Systems. – In: Proc. of 10th International Conference on Intelligent User Interfaces, ACM, 2005, pp. 167-174.10.1145/1040830.1040870
- 31. Alodhaibi, K. Decision-Guided Recommenders with Composite Alternatives. Information Technology, George Mason University, Virginia, 2011.
- 32. Aggarwal, C. C. Evaluating Recommender Systems. – In: Recommender Systems: The Textbook, Springer International Publishing, Cham, 2016, pp. 225-254.10.1007/978-3-319-29659-3_7
- 33. Adomavicius, G., Y. O. Kwon. New Recommendation Techniques for Multicriteria Rating Systems. – IEEE Intelligent Systems, Vol. 22, 2007, No 3, pp. 48-55.10.1109/MIS.2007.58