Cart (Loading....) | Create Account
Close category search window
 

A novel approach to solve the sparsity problem in collaborative filtering

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
$31 $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)

Collaborative Filtering (CF) is the most successful approach of Recommender System. Although it has made significant progress over the last decade, the current CF method is stressed by the sparsity problem. In this paper we propose a novel approach to address this issue. Multiple Imputation (MI) is a useful statistic strategy for dealing with data sets with missing values and replace each missing value with a set of plausible values that represent the uncertainty about the right value. In our approach we apply MI technique in the data processing procedure to turn the original sparse data into dense data. And then we use the dense data and the original data in the following CF progress separately. We compare their performance both in cosine-based and correlation-based similarity measures. We conduct a 10-fold cross validation and take the MAE as the evaluation metrics. Our experimental results show that our approach can efficiently solve the extreme sparsity problem, and provide better recommendation results than traditional CF method.

Published in:

Networking, Sensing and Control (ICNSC), 2010 International Conference on

Date of Conference:

10-12 April 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.