文章基本信息

标题：Implementation of Users Approach for Item Prediction and Its Recommendation in Ecommerce
本地全文：下载
作者：Himanshu M. Deulkar ; Prof. Rajeshri R. Shelke
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：4
页码：7271
DOI：10.15680/IJIRCCE.2017.05040134
出版社：S&S Publications
摘要：Nowadays, a large number of new online businesses emerge rapidly. Although user data are extremely sparse in the e-commerce site, abundant knowledge in the more developed review site can be utilized to help Missing Item prediction and its recommendation based on users approach. Predicting the missing items form dataset is indefinite area of research in Web Data Mining. Current approaches use association rule mining techniques which are applied to only small item sets. Numbers of mechanisms were intended for frequent item sets but less attention has been paid that take the advantage of these frequent item sets for prediction purpose. In order to reduce the rule mining cost for large dataset & to provide online prediction efficiently, the proposed approach use novel method for predicting the missing items. The proposed approach extends advantages of prediction at a higher level of abstraction and reduced rule generation complexity by finding out a technique that will work on dissimilar approach.
关键词：Predicting the missing items in ecommerce; Missing Item Recommendation; Item recommendation in;online business.