Comparative study of personalizing recommender systems based on shopping system

Zhihang Tang, Zhonghua Wen

School of Computer and Communication, Hunan Institute of Engineering Xiangtan 411104, China

Making choices is an integral part of everyday life; Recommender systems facilitate decision-making processes through informed assistance and enhanced user experience. To aid in the decision-making process, recommender systems use the available data on the items themselves, Personalized recommender systems subsequently use this input data, and convert it to an output in the form of ordered lists or scores of items in which a user might be interested. These lists or scores are the final result the user will be presented with, and their goal is to assist the user in the decision-making process. Recommender systems facilitate making choices, improve user experience, and increase revenue, therefore should be easily accessible for deployment to interested parties. The implementation of recommender systems in RapidMiner has been additionally simplified through the Recommender Extension.