A NoSQL Approach for Recommendation of Highly Rated Products
Andreas Kanavos, Leonidas Theodorakopoulos, Gerasimos Vonitsanos · Zenodo (CERN European Organization for Nuclear Research) · 2019
Nowadays there is a growing need for collecting and processing data from different sources in heterogeneous and semi-structured formats. Developing an intelligent recommendation system is a good way to overcome the problem of overloaded products information provided by the e-commerce enterprises. As there are a great number of products on the Internet, it is impossible to recommend all kinds of products in one system. We believe that the personalized recommendation system should be built up according to the special features of a certain sort of product, and forming professional recommendation systems for different products. In this paper, based on the consumer’s current needs obtained from the system-user interactions, we present a NoSQL database approach for modeling consumer electronics to retrieve optimal products. We built a robust analytics framework by integrating Apache Spark with Apache Cassandra and in following utilize data mining techniques for presenting a model capable of recommending highly rated products