By Topic

A Framework for Recommender Systems in E-Commerce Based on Distributed Storage and Data-Mining

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Changjian Fu ; Sch. of Bus., Xiangtan Univ., Xiangtan, China ; Zhihua Leng

Recent technological advances in many networks and applications, particularly the Internet and the World Wide Web (WWW), have generated a huge amount of information available to users. A recommender system in E-Commerce is an intermediary program (or an agent) with a user interface that automatically and intelligently generates a list of information which suits an individual's needs. In this paper, a new recommender system framework based on Distributed Storage and Data-Mining is proposed. The recommendation systems work by collecting data from users, commodities, and transactions, examples of implicit data collection include the following: observing the items that a user views in an online store, keeping a record of the items that a user purchases online, obtaining a list of items that a user has listened to or watched on his/her computer, et al. This data ared stored by hadoop distributed file system(HDFS), and mined in Map Reduce that is a programming model and an associated implementation for processing and generating large data sets. The patterns (i.e., classification, cluster, and association rule ared used to complete recommender by hybrid recommendation method including (1) by analyzing the information content, i.e.), content-based filtering, (2) by referencing other user access behaviors, i.e., collaborative filtering, and (3) by referencing relations between commodity, i.e., association rule-based recommendation.

Published in:

E-Business and E-Government (ICEE), 2010 International Conference on

Date of Conference:

7-9 May 2010