Learning neighbourhood-based collaborative filtering parameters

J. Griffith; C. O'Riordan; H. Sorensen

Learning neighbourhood-based collaborative filtering parameters

J. Griffith
, C. O'Riordan
, H. Sorensen

Computer Science

University College Cork

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

The work outlined in this paper uses a genetic algorithm to learn the optimal set of parameters for a neighbourhood-based collaborative filtering approach. The motivation is firstly to re-assess whether the default parameter values often used are valid and secondly to assess whether different datasets require different parameter settings. Three datasets are considered in this initial investigation into the approach: Movielens, Bookcrossing and Lastfm.

Original language	English
Title of host publication	KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval
Pages	452-455
Number of pages	4
Publication status	Published - 2011
Event	International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011 - Paris, France Duration: 26 Oct 2011 → 29 Oct 2011

Publication series

Name	KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Conference

Conference	International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011
Country/Territory	France
City	Paris
Period	26/10/11 → 29/10/11

Keywords

Collaborative filtering
Genetic algorithms
Neighbourhood-based approach

Cite this

@inproceedings{930cbdbd06584a0d8e24fe656085f010,

title = "Learning neighbourhood-based collaborative filtering parameters",

abstract = "The work outlined in this paper uses a genetic algorithm to learn the optimal set of parameters for a neighbourhood-based collaborative filtering approach. The motivation is firstly to re-assess whether the default parameter values often used are valid and secondly to assess whether different datasets require different parameter settings. Three datasets are considered in this initial investigation into the approach: Movielens, Bookcrossing and Lastfm.",

keywords = "Collaborative filtering, Genetic algorithms, Neighbourhood-based approach",

author = "J. Griffith and C. O'Riordan and H. Sorensen",

year = "2011",

language = "English",

isbn = "9789898425799",

series = "KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval",

pages = "452--455",

booktitle = "KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval",

note = "International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011 ; Conference date: 26-10-2011 Through 29-10-2011",

}

Griffith, J , O'Riordan, C & Sorensen, H 2011, Learning neighbourhood-based collaborative filtering parameters. in KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 452-455, International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011, Paris, France, 26/10/11.

Learning neighbourhood-based collaborative filtering parameters. / Griffith, J.; O'Riordan, C.; Sorensen, H.
KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. 2011. p. 452-455 (KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

TY - GEN

T1 - Learning neighbourhood-based collaborative filtering parameters

AU - Griffith, J.

AU - O'Riordan, C.

AU - Sorensen, H.

PY - 2011

Y1 - 2011

N2 - The work outlined in this paper uses a genetic algorithm to learn the optimal set of parameters for a neighbourhood-based collaborative filtering approach. The motivation is firstly to re-assess whether the default parameter values often used are valid and secondly to assess whether different datasets require different parameter settings. Three datasets are considered in this initial investigation into the approach: Movielens, Bookcrossing and Lastfm.

AB - The work outlined in this paper uses a genetic algorithm to learn the optimal set of parameters for a neighbourhood-based collaborative filtering approach. The motivation is firstly to re-assess whether the default parameter values often used are valid and secondly to assess whether different datasets require different parameter settings. Three datasets are considered in this initial investigation into the approach: Movielens, Bookcrossing and Lastfm.

KW - Collaborative filtering

KW - Genetic algorithms

KW - Neighbourhood-based approach

UR - https://www.scopus.com/pages/publications/84862232033

M3 - Conference Publication

SN - 9789898425799

T3 - KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

SP - 452

EP - 455

BT - KDIR 2011 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

T2 - International Conference on Knowledge Discovery and Information Retrieval, KDIR 2011

Y2 - 26 October 2011 through 29 October 2011

ER -

Learning neighbourhood-based collaborative filtering parameters

Abstract

Publication series

Conference

Keywords

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Cite this