期刊名称：International Journal of Information Engineering and Electronic Business
印刷版ISSN：2074-9023
电子版ISSN：2074-9031
出版年度：2019
卷号：11
期号：6
页码：24-31
DOI：10.5815/ijieeb.2019.06.04
出版社：MECS Publisher
摘要：The buying behavior of the consumer is grown nowadays through recommender systems. Though it recommends, still there are limitations to give a recommendation to the users. In order to address data sparsity and scalability, a hybrid approach is developed for the effective recommendation in this paper. It combines the feature engineering attributes and collaborative filtering for prediction. The proposed system implemented using supervised learning algorithms. The results empirically proved that the mean absolute error of prediction was reduced. This approach shows very promising results.
