LibRec: A Java library for recommender systems

Guibing Guo; Jie Zhang; Zhu Sun; Neil Yorke-Smith

LibRec: A Java library for recommender systems

Guibing Guo, Jie Zhang, Zhu Sun, Neil Yorke-Smith

Research output: Contribution to journal › Conference article › Scientific › peer-review

79 Citations (SciVal)

20 Downloads (Pure)

Abstract

The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.

Original language	English
Number of pages	4
Journal	CEUR Workshop Proceedings
Volume	1388
Publication status	Published - 1 Jul 2015
Externally published	Yes
Event	23rd Conference on User Modeling, Adaptation, and Personalization, UMAP 2015 - Dublin, Ireland Duration: 29 Jun 2015 → 3 Jul 2015

Cite this

@article{35b593b2031e46b59ddfdaac6089691d,

title = "LibRec: A Java library for recommender systems",

abstract = "The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.",

author = "Guibing Guo and Jie Zhang and Zhu Sun and Neil Yorke-Smith",

year = "2015",

month = jul,

day = "1",

language = "English",

volume = "1388",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "23rd Conference on User Modeling, Adaptation, and Personalization, UMAP 2015 ; Conference date: 29-06-2015 Through 03-07-2015",

}

TY - JOUR

T1 - LibRec

T2 - 23rd Conference on User Modeling, Adaptation, and Personalization, UMAP 2015

AU - Guo, Guibing

AU - Zhang, Jie

AU - Sun, Zhu

AU - Yorke-Smith, Neil

PY - 2015/7/1

Y1 - 2015/7/1

N2 - The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.

AB - The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.

UR - http://www.scopus.com/inward/record.url?scp=85072775238&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85072775238

SN - 1613-0073

VL - 1388

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 29 June 2015 through 3 July 2015

ER -

LibRec: A Java library for recommender systems

Abstract

Other files and links

Fingerprint

Cite this