文章基本信息

标题：Analysis and Implementation of Recommender System in E-Commerce
本地全文：下载
作者：Shubha C A ; Shubha Bhat ; Anjan K Koundinya 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2237&2238
页码：143-148
出版社：Newswood and International Association of Engineers
摘要：Astounding growth of E-Commerce in the business arena, is the outcome of boundless exploration in the field of Recommender Systems (RS). RS’s have increased customer engagement of Video Streaming applications by 23% and have a market of over 450 billion dollars. The immense growth of products as well as customers poses crucial challenges to RS. Millions of customers and products exist in the E-Commerce scenario and are generating high quality recommendations. To perform several recommendations in a fraction of second is a demanding and compelling task. The aim of this paper is to analyze various techniques that fetch personalized recommendations in e-commerce systems which are web based. Evidently, three techniques could be used to calculate the prediction values for a given set of users and items. Collaborative filtering technique, content based filtering technique and a hybrid approach persists in the realm of recommendations. For a large user base consisting of several transactions, analysis of RS will be outcome of thorough scrutiny of memory and model based algorithms. The dimensionality of the data is the key for analysis of the required and relevant data for the user’s context. Ultimately the best suited algorithm for the given data set is found to give recommendations to the user through an interactive webbased user interface. Finally, a convenient evaluation technique is used to check the accuracy of the recommendations generated with the algorithms.
关键词：E;commerce; Recommender system; Collaborative filtering; Content;based filtering;