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

Collaborative Filtering with Personalized Skylines

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

3 Author(s)
Bartolini, I. ; DEIS, Univ. di Bologna, Bologna, Italy ; Zhenjie Zhang ; Papadias, D.

Collaborative filtering (CF) systems exploit previous ratings and similarity in user behavior to recommend the top-k objects/records which are potentially most interesting to the user assuming a single score per object. However, in various applications, a record (e.g., hotel) maybe rated on several attributes (value, service, etc.), in which case simply returning the ones with the highest overall scores fails to capture the individual attribute characteristics and to accommodate different selection criteria. In order to enhance the flexibility of CF, we propose Collaborative Filtering Skyline (CFS), a general framework that combines the advantages of CF with those of the skyline operator. CFS generates a personalized skyline for each user based on scores of other users with similar behavior. The personalized skyline includes objects that are good on certain aspects, and eliminates the ones that are not interesting on any attribute combination. Although the integration of skylines and CF has several attractive properties, it also involves rather expensive computations. We face this challenge through a comprehensive set of algorithms and optimizations that reduce the cost of generating personalized skylines. In addition to exact skyline processing, we develop an approximate method that provides error guarantees. Finally, we propose the top-k personalized skyline, where the user specifies the required output cardinality.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:23 ,  Issue: 2 )

Date of Publication:

Feb. 2011

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.