Open AccessHybrid Recommendation System for Better Mining Rules Generation of User and Consumer Data
Author(s) -
Gaurav Gupta,
Atul Dattatrya Newase
Publication year - 2020
Publication title -
bsss journal of computer/bsss journal of computer
Language(s) - English
Resource type - Journals
eISSN - 2582-4880
pISSN - 0975-7228
DOI - 10.51767/jc1110
Subject(s) - computer science , sorting , recommender system , field (mathematics) , focus (optics) , software , quality (philosophy) , data mining , task (project management) , sort , product (mathematics) , hybrid system , data quality , data science , database , information retrieval , machine learning , engineering , philosophy , physics , geometry , mathematics , systems engineering , epistemology , pure mathematics , optics , programming language , metric (unit) , operations management
In today's world, data and information play an essential role in each field, including online and software data. However, it is a challenging task to abstract & sorted consumer data for use. To solve this data overloading and sorting of useful data, a Hybrid Recommendation System (HRS) comes into existence. HRS's focus is to suggest the best applicable and useful items to the related customers or users. The recommendations can be applied to decision-making processes, like which types of things to get, which new videos to watch, which online latest games and software to search, or the best product. The benefits of the Hybrid Recommendation System persist on the quality efficiency of the system. The efficient things can be calculated in easy to use, reliable, accurate, and expandable. This proposed HRS's primary goal is to better mining rules based on user and consumer data to improve the Hybrid Recommendation System's accuracy.