Collaborative Filtering Ensemble for Personalized Name Recommendation

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

Organisationseinheiten

Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names
UntertitelProceedings of the ECML PKDD Discovery Challenge - Recommending Given Names co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML PKDD 2013)
Seiten37-48
Seitenumfang12
PublikationsstatusVeröffentlicht - 2013
Veranstaltung15th ECML PKDD Discovery Challenge - Recommending Given Names, 20DC13 2013 - Prague, Tschechische Republik
Dauer: 27 Sept. 201327 Sept. 2013

Publikationsreihe

NameCEUR Workshop Proceedings
Herausgeber (Verlag)CEUR Workshop Proceedings
Band1120
ISSN (Print)1613-0073

Abstract

Out of thousands of names to choose from, picking the right one for your child is a daunting task. In this work, our objective is to help parents making an informed decision while choosing a name for their baby. We follow a recommender system approach and combine, in an ensemble, the individual rankings produced by simple collaborative filtering algorithms in order to produce a personalized list of names that meets the individual parents' taste. Our experiments were conducted using real-world data collected from the query logs of nameling (nameling.net), an online portal for searching and exploring names, which corresponds to the dataset released in the context of the ECML PKDD Discover Challenge 2013. Our approach is intuitive, easy to implement, and features fast training and prediction steps.

ASJC Scopus Sachgebiete

Zitieren

Collaborative Filtering Ensemble for Personalized Name Recommendation. / Coma-Puig, Bernat; Diaz-Aviles, Ernesto; Nejdl, Wolfgang.
20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names: Proceedings of the ECML PKDD Discovery Challenge - Recommending Given Names co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML PKDD 2013). 2013. S. 37-48 (CEUR Workshop Proceedings; Band 1120).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Coma-Puig, B, Diaz-Aviles, E & Nejdl, W 2013, Collaborative Filtering Ensemble for Personalized Name Recommendation. in 20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names: Proceedings of the ECML PKDD Discovery Challenge - Recommending Given Names co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML PKDD 2013). CEUR Workshop Proceedings, Bd. 1120, S. 37-48, 15th ECML PKDD Discovery Challenge - Recommending Given Names, 20DC13 2013, Prague, Tschechische Republik, 27 Sept. 2013. <http://ceur-ws.org/Vol-1120/paper3.pdf>
Coma-Puig, B., Diaz-Aviles, E., & Nejdl, W. (2013). Collaborative Filtering Ensemble for Personalized Name Recommendation. In 20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names: Proceedings of the ECML PKDD Discovery Challenge - Recommending Given Names co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML PKDD 2013) (S. 37-48). (CEUR Workshop Proceedings; Band 1120). http://ceur-ws.org/Vol-1120/paper3.pdf
Coma-Puig B, Diaz-Aviles E, Nejdl W. Collaborative Filtering Ensemble for Personalized Name Recommendation. in 20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names: Proceedings of the ECML PKDD Discovery Challenge - Recommending Given Names co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML PKDD 2013). 2013. S. 37-48. (CEUR Workshop Proceedings).
Coma-Puig, Bernat ; Diaz-Aviles, Ernesto ; Nejdl, Wolfgang. / Collaborative Filtering Ensemble for Personalized Name Recommendation. 20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names: Proceedings of the ECML PKDD Discovery Challenge - Recommending Given Names co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML PKDD 2013). 2013. S. 37-48 (CEUR Workshop Proceedings).
Download
@inproceedings{dc32d3bb35fd4a7e8582117735dc47b3,
title = "Collaborative Filtering Ensemble for Personalized Name Recommendation",
abstract = "Out of thousands of names to choose from, picking the right one for your child is a daunting task. In this work, our objective is to help parents making an informed decision while choosing a name for their baby. We follow a recommender system approach and combine, in an ensemble, the individual rankings produced by simple collaborative filtering algorithms in order to produce a personalized list of names that meets the individual parents' taste. Our experiments were conducted using real-world data collected from the query logs of nameling (nameling.net), an online portal for searching and exploring names, which corresponds to the dataset released in the context of the ECML PKDD Discover Challenge 2013. Our approach is intuitive, easy to implement, and features fast training and prediction steps.",
keywords = "Given name recommendation, Personalized ranking, Top-N recommendation",
author = "Bernat Coma-Puig and Ernesto Diaz-Aviles and Wolfgang Nejdl",
year = "2013",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR Workshop Proceedings",
pages = "37--48",
booktitle = "20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names",
note = "15th ECML PKDD Discovery Challenge - Recommending Given Names, 20DC13 2013 ; Conference date: 27-09-2013 Through 27-09-2013",

}

Download

TY - GEN

T1 - Collaborative Filtering Ensemble for Personalized Name Recommendation

AU - Coma-Puig, Bernat

AU - Diaz-Aviles, Ernesto

AU - Nejdl, Wolfgang

PY - 2013

Y1 - 2013

N2 - Out of thousands of names to choose from, picking the right one for your child is a daunting task. In this work, our objective is to help parents making an informed decision while choosing a name for their baby. We follow a recommender system approach and combine, in an ensemble, the individual rankings produced by simple collaborative filtering algorithms in order to produce a personalized list of names that meets the individual parents' taste. Our experiments were conducted using real-world data collected from the query logs of nameling (nameling.net), an online portal for searching and exploring names, which corresponds to the dataset released in the context of the ECML PKDD Discover Challenge 2013. Our approach is intuitive, easy to implement, and features fast training and prediction steps.

AB - Out of thousands of names to choose from, picking the right one for your child is a daunting task. In this work, our objective is to help parents making an informed decision while choosing a name for their baby. We follow a recommender system approach and combine, in an ensemble, the individual rankings produced by simple collaborative filtering algorithms in order to produce a personalized list of names that meets the individual parents' taste. Our experiments were conducted using real-world data collected from the query logs of nameling (nameling.net), an online portal for searching and exploring names, which corresponds to the dataset released in the context of the ECML PKDD Discover Challenge 2013. Our approach is intuitive, easy to implement, and features fast training and prediction steps.

KW - Given name recommendation

KW - Personalized ranking

KW - Top-N recommendation

UR - http://www.scopus.com/inward/record.url?scp=84922016798&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84922016798

T3 - CEUR Workshop Proceedings

SP - 37

EP - 48

BT - 20DC13 2013 ECML PKDD Discovery Challenge - Recommending Given Names

T2 - 15th ECML PKDD Discovery Challenge - Recommending Given Names, 20DC13 2013

Y2 - 27 September 2013 through 27 September 2013

ER -

Von denselben Autoren