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Extending user profiles in collaborative filtering algorithms to alleviate the sparsity problem

Toon De Pessemier UGent, Kris Vanhecke UGent, Simon Dooms UGent, Tom Deryckere UGent and Luc Martens UGent (2011) LECTURE NOTES IN BUSINESS INFORMATION PROCESSING. 75(4). p.230-244
abstract
The overabundance of information and the related difficulty to discover interesting content has complicated the selection process for end-users. Recommender systems try to assist in this content-selection process by using intelligent personalisation techniques which filter the information. Most commonly-used recommendation algorithms are based on Collaborative Filtering (CF). However, present-day CF techniques are optimized for suggesting provider-generated content and partially lose their effectiveness when recommending user-generated content. Therefore, we propose an advanced CF algorithm which considers the specific characteristics of user-generated content (like the sparsity of the data matrix). To alleviate this sparsity problem, profiles are extended with probable future consumptions. These extended profiles increase the profile overlap probability, thereby increasing the number of neighbours used for calculating the recommendations. This way, the recommendations become more precise and diverse compared to traditional CF recommendations. This paper explains the proposed algorithm in detail and demonstrates the improvements on standard CF.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Recommender system, Collaborative filtering, Personalisation, Algorithm, Sparsity, TV
in
LECTURE NOTES IN BUSINESS INFORMATION PROCESSING
Lect. notes bus. inf. process.
editor
Joaquim Filip and José Cordeiro
volume
75
issue
4
issue title
Web information systems and technologies
pages
230 - 244
publisher
Springer
place of publication
Berlin, Germany
conference name
6th International Conference on Web Information Systems and Technologies (WEBIST - 2010)
conference location
Valencia, Spain
conference start
2010-04-07
conference end
2010-04-10
Web of Science type
Proceedings Paper
Web of Science id
000301969400017
ISSN
1865-1348
ISBN
9783642228094
DOI
10.1007/978-3-642-22810-0_17
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2040243
handle
http://hdl.handle.net/1854/LU-2040243
date created
2012-02-22 11:55:55
date last changed
2012-06-06 09:48:45
@inproceedings{2040243,
  abstract     = {The overabundance of information and the related difficulty to discover interesting content has complicated the selection process for end-users. Recommender systems try to assist in this content-selection process by using intelligent personalisation techniques which filter the information. Most commonly-used recommendation algorithms are based on Collaborative Filtering (CF). However, present-day CF techniques are optimized for suggesting provider-generated content and partially lose their effectiveness when recommending user-generated content. Therefore, we propose an advanced CF algorithm which considers the specific characteristics of user-generated content (like the sparsity of the data matrix). To alleviate this sparsity problem, profiles are extended with probable future consumptions. These extended profiles increase the profile overlap probability, thereby increasing the number of neighbours used for calculating the recommendations. This way, the recommendations become more precise and diverse compared to traditional CF recommendations. This paper explains the proposed algorithm in detail and demonstrates the improvements on standard CF.},
  author       = {De Pessemier, Toon and Vanhecke, Kris and Dooms, Simon and Deryckere, Tom and Martens, Luc},
  booktitle    = {LECTURE NOTES IN BUSINESS INFORMATION PROCESSING},
  editor       = {Filip, Joaquim and Cordeiro, Jos{\'e}},
  isbn         = {9783642228094},
  issn         = {1865-1348},
  keyword      = {Recommender system,Collaborative filtering,Personalisation,Algorithm,Sparsity,TV},
  language     = {eng},
  location     = {Valencia, Spain},
  number       = {4},
  pages        = {230--244},
  publisher    = {Springer},
  title        = {Extending user profiles in collaborative filtering algorithms to alleviate the sparsity problem},
  url          = {http://dx.doi.org/10.1007/978-3-642-22810-0\_17},
  volume       = {75},
  year         = {2011},
}

Chicago
De Pessemier, Toon, Kris Vanhecke, Simon Dooms, Tom Deryckere, and Luc Martens. 2011. “Extending User Profiles in Collaborative Filtering Algorithms to Alleviate the Sparsity Problem.” In Lecture Notes in Business Information Processing, ed. Joaquim Filip and José Cordeiro, 75:230–244. Berlin, Germany: Springer.
APA
De Pessemier, T., Vanhecke, K., Dooms, S., Deryckere, T., & Martens, L. (2011). Extending user profiles in collaborative filtering algorithms to alleviate the sparsity problem. In J. Filip & J. Cordeiro (Eds.), LECTURE NOTES IN BUSINESS INFORMATION PROCESSING (Vol. 75, pp. 230–244). Presented at the 6th International Conference on Web Information Systems and Technologies (WEBIST - 2010), Berlin, Germany: Springer.
Vancouver
1.
De Pessemier T, Vanhecke K, Dooms S, Deryckere T, Martens L. Extending user profiles in collaborative filtering algorithms to alleviate the sparsity problem. In: Filip J, Cordeiro J, editors. LECTURE NOTES IN BUSINESS INFORMATION PROCESSING. Berlin, Germany: Springer; 2011. p. 230–44.
MLA
De Pessemier, Toon, Kris Vanhecke, Simon Dooms, et al. “Extending User Profiles in Collaborative Filtering Algorithms to Alleviate the Sparsity Problem.” Lecture Notes in Business Information Processing. Ed. Joaquim Filip & José Cordeiro. Vol. 75. Berlin, Germany: Springer, 2011. 230–244. Print.