References
- MalekiShoja, B., N. Tabrizi. Customer Reviews Analysis with Deep Neural Networks for e-Commerce Recommender Systems. – In: IEEE Access, Vol. 7, 2019, pp. 119121-119130. DOI: 10.1109/ACCESS.2019.2937518.
- 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. DOI: 10.1109/ACCESS.2020.3005953.
- Won, H., Y. Lee, J. Shim, H. Ahn. A Hybrid Collaborative Filtering Model Using Customer Search Keyword Data for Product Recommendation. – In: Proc. of 18th IEEE International Conference on Machine Learning and Applications (ICMLA’19), Boca Raton, FL, USA, 2019, pp. 1523-1526. DOI: 10.1109/ICMLA.2019.00251.
- Shapira, B. Recommender Systems Handbook. USA, Springer, 2015.
- Xue, H.-J., X. Dai, J. Zhang, S. Huang, J. Chen. Deep Matrix Factorization Models for Recommender Systems. – In: Proc. of 26th International Joint Conference on Artificial Intelligence (IJCAI’17), 2017, pp. 3203-3209. DOI: 10.24963/ijcai.2017/447.
- Cacheda, F., V. Carneiro, D. Fernandez, V. Formoso. Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-Performance Recommender Systems. TWEB. 5. 2, 2011.
- Sony, H., G. Poonam. Survey Paper on Recommendation System Using Data Mining Techniques. – International Journal of Engineering and Computer Science, 2016. DOI: 10.18535/ijecs/v5i5.60.
- Yong, S., A. Yasuhito. Mining Relationship Between User Purposes and Product Features Towards Purpose-Oriented Recommendation. 2017, pp. 9-21. DOI: 10.1007/978-3-319-61845-6-2.
- Fatoni, C., E. Utami, F. Wibowo. Online Store Product Recommendation System Uses Apriori Method. – Journal of Physics: Conference Series, 2018. 1140. 012034. 10.1088/1742-6596/1140/1/012034.
- Priyanka, M. J., A. Shetty, D. Vora. Web Usage Mining Using Pearson’s Correlation Coefficient. – International Journal of Engineering Research and Applications (IJERA), Vol. 3, March-April 2013, Issue 2, pp. 676-679. ISSN: 2248-9622. www.ijera.com
- Tahmasebi, F., M. Meghdadi, S. Ahmadian, et al. A Hybrid Recommendation System Based on Profile Expansion Technique to Alleviate Cold Start Problem. – Multimed Tools Appl., Vol. 80, 2021, pp. 2339-2354.
- Feng, J., Z. Xia, X. Feng, J. Peng. RBPR: A Hybrid Model for the New User Cold Start Problem in Recommender Systems. – Knowledge-Based Systems, Vol. 214, 2021, 106732. ISSN: 0950-7051.
- Huang, L., M. Fu, F. Li, H. Qu, Y. Liu, W. Chen. A Deep Reinforcement Learning Based Long-Term Recommender System. – Knowledge-Based Systems, Vol. 213, 2021, 106706. ISSN: 0950-7051.
- Natarajan, S., S. Vairavasundaram, S. Natarajan, A. H. Gandomi. Resolving Data Sparsity and Cold Start Problem in Collaborative Filtering Recommender System Using Linked Open Data. – Expert Systems with Applications, Vol. 149, 2020, 113248. ISSN: 0957-4174.
- Verma, D., K. Gulati, R. R. Shah. Addressing the Cold-Start Problem in Outfit Recommendation Using Visual Preference Modelling. – In: Proc. of 6th IEEE International Conference on Multimedia Big Data (BigMM’20), 2020, pp. 251-256. DOI: 10.1109/BigMM50055.2020.00043.
- A Combined Approach for Collaborative Filtering Based Recommender Systems with Matrix Factorisation and Outlier Detection. – Journal of Business Analytics, Vol. 4, July 2021, No 19, 1-14. DOI:10.1080/2573234X.2021.1947752.
- Bansal, P., S. S. Gill. Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm. – Cybernetics and Information Technologies, Vol. 22, 2022, No 4, pp. 167-177.
- Shambour, Q. Y., N. M. Turab, O. Y. Adwan. An Effective e-Commerce Recommender System Based on Trust and Semantic Information. – Cybernetics and Information Technologies, Vol. 21, 2021, No 1, pp. 103-118.
- Premalatha, M., V. Viswanathan. Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms. – Cybernetics and Information Technologies, Vol. 19, 2019, No 3, pp. 25-44.