HYBRID RECOMMENDATION SYSTEM FOR BETTER MINING RULES GENERATION OF USER AND CONSUMER DATA
In today’s world Data and information playing an important role in each field including online and software data. However, it is very difficult 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. The focus of HRS is to suggest the best applicable and useful items to the related customers or user. The recommendations can be applied to decision-making processes, like which types of things to get, which new videos to watch to, which online latest games and software to search, or which is the best product among all. The benefits of Hybrid Recommendation System persist on quality efficiency of the system. The efficient things can be calculated in the forms of easy to use, reliable accurate and expandable. The main goal of this proposed HRS is to better mining rules based on user and consumer data to improve the accuracy of Hybrid Recommendation System.