On demand recommendation using association rule mining approach
Mayur Bhosale, Tushar H. Ghorpade, Rajashree Shedge · 2016
In these day, as the e-commerce industry is growing and becoming complex, everyone uses online websites for getting reviews and giving the reviews on the website in the form of comments. This comment varies from worst level to best level. So in order to categorize these comments or to predict the best outcome among the posted comments recommendation is needed there is a need for recommendation system. Collaborative filtering is currently most widely used approach to build recommendation system. It require users to express opinions on items and then they collect opinions and recommend items based on peoples opinions similarity. Those who agree most are the contributors. Recommendation system applies information retrieval technique to select online information relevant to a given user. We proposed hybrid recommendation system, where we are considering external feedback of users to recommend cars in market. we are considering technical words related to car in the data-set itself, so that it will be easy for categorization of car and gives rating to a car. In proposed system recommendation of all users are combined and this recommendation are used to categorized the users region wise, with the help of region data-set. Also used Association rule mining for finding On-demand car in market which gives faster result for large number of data-set.