English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Unifying Colloborative and Content-Based Filtering.

Basilico, J., & Hofmann, T. (2004). Unifying Colloborative and Content-Based Filtering. Proceedings of the 21st International Conference on Machine Learning, 65.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-F376-4 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-F377-2
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Basilico, J, Author
Hofmann, T1, Author              
Greiner D. Schuurmans, R., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 Abstract: Collaborative and content-based filtering are two paradigms that have been applied in the context of recommender systems and user preference prediction. This paper proposes a novel, unified approach that systematically integrates all available training information such as past user-item ratings as well as attributes of items or users to learn a prediction function. The key ingredient of our method is the design of a suitable kernel or similarity function between user-item pairs that allows simultaneous generalization across the user and item dimensions. We propose an on-line algorithm (JRank) that generalizes perceptron learning. Experimental results on the EachMovie data set show significant improvements over standard approaches.

Details

show
hide
Language(s):
 Dates: 2004
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2739
 Degree: -

Event

show
hide
Title: ICLM 2004
Place of Event: Banff, Alberta, Canada
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 21st International Conference on Machine Learning
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 65 Identifier: -