Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Akhter, Md Hesam
- Machine Learning Techniques To Recommend Products In E-Commerce: A Systematic Review
Abstract Views :130 |
PDF Views:0
Authors
Affiliations
1 Al-Falah University, Haryana, IN
1 Al-Falah University, Haryana, IN
Source
International Journal on Customer Relations, Vol 9, No 1&2 (2021), Pagination: 23-27Abstract
Product recommender systems have been an effective approach to overcoming information overload on the Web, with the growing size of online statistics, as recommending the right product based on consumer liking became challenging for e-commerce businesses. The machine learning techniques can be applied to solve it. However, due to the large number of algorithms available in the literature, it is quite difficult to select a suitable machine learning algorithm. Researchers have little information about the best approaches to develop recommender systems for e-commerce using machine learning. Here, we have presented our work as a systematic review of the literature, which surveys to choose machine learning algorithms to recommend products in e-commerce and recognise research opportunities for the researchers in developing recommender systems. The survey concluded that deep learning and neural networks techniques are widely used to predict the right products for recommendation to the customers in e-commerce, because they can be very good at recognising patterns in a way similar to the human brain.Keywords
Machine Learning, Product Recommendation, E-Commerce, Systematic Review
References
- Felden, C., & Chamoni, P. (2007, January). Recommender systems based on an active data warehouse with text documents. In System Sciences, 2007. HICSS 2007. 40th Annual Hawaii International Conference on (pp. 168a168a), IEEE.
- Tewari, A. S., Kumar, A., & Barman, A. G. (2014).Recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. IEEE (pp. 500-503).
- Maheswari, K., & Priya, P. (2017). Predicting customer behavior in online shopping using SVM classifier. In IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (p. 5), IEEE.
- Columbus, L. (2018, December). 10 ways machine learning is revolutionizing sales. Enterprise and Cloud, p. 10.
- Kulkarni, S. (Ed.) (2012). Machine learning algorithms for problem solving in computational applications, intelligent techniques. IGI Global.
- Murali, M. V., Vishnu, T. G., & Victor, N. (2019). A collaborative filtering based recommender system for suggesting new trends in any domain of research. In International Conference on Advanced Computing & Communication System (ICACCS ).
- Bellogín, A., Castells, P., & Cantador, I. (2011). Precisionoriented evaluation of recommender systems: An algorithmic comparison. ACM, 978-1-4503-0683-6.
- Marović, M., Mihoković, M., Mikša, M., Pribil, S., & Tus, A. (2011). Automatic movie ratings prediction using machine learning. MIPRO, Proceedings of the 34th International Convention (pp. 1640-1645), IEEE.
- Wei, C. P., Yang, C. S., & Hsiao, H. W. (2008). A collaborative filtering-based approach to personalized document clustering. Decision Supp. Syst., 45(3).
- Adebola, O., & Bukola, O. (2019). Predicting consumer behavior in digital market: A machine learning approach.International Journal of Innovative Research in Science, Engineering and Technology, 8(8).
- Kim, J., & Ahn, H. (2009). A new perspective for neural networks: Application to a marketing management problem. Journal of Information Science and Engineering, 25, 1605-1616.
- Canny, J. (2002). Collaborative filtering with privacy via factor analysis. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 238-245). Finland, Tampere: ACM Press.
- Bai, B., Fan, Y., Tan, W., & Zhang, J. (2017). A deep learning framework for recommendation of long-tail web services.In IEEE Transactions on Services Computing, 2017.
- Bujang, M. A., Sa’at, N., & Sidik, T. M. I. T. A. B. (2017).Determination of minimum sample size requirement for multiple linear regression and analysis of covariance based on experimental and non-experimental studies. Epidemiology Biostatistics and Public Health, 14(3).
- Khade, A. A. (2016). Performing customer behavior analysis using big data analytics. In 7th International Conference on Communication, Computing and Virtualization (p. 7), Science Direct.
- Alasadi, S. (2017). Review of data preprocessing techniques in data mining. Journal of Engineering and Applied Sciences, 12(16), 4102-4107.
- Eichinger, F., Nauck, D., & Klawonn, F. (2006). Sequence mining for customer behavior predictions in telecommunications for marketing in a digital era. European Management Journal, 32(1), 112.
- Fullerton, R. A. (2013). The birth of consumer behavior: Motivation research in the 1950s. Journal of Historical Research in Marketing, 5(2), 212-222.
- Ericson, K., & Pallickara, S. (213). On the performance of high dimensional data clustering and classification algorithms. Future Generation Computer Systems, 29(4),1024-1034.
- Egghe, L., & Leydesdorff, L. (2009). The relation between Pearson’s correlation coefficient r and Salton’s cosine measure. Journal of the American Society for Information Science and Technology, 60(5), 1027-1036.
- Lucas, J. P., Segrera, S., & Moreno, M. N. (2012). Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems. Expert Systems with Applications, 39(1), 1273-1283