RecSys Challenge 2016

Fabian Abel; András Benczúr; Daniel Kohlsdorf; Martha Larson; Róbert Pálovics

doi:10.1145/2959100.2959207

RecSys Challenge 2016

Fabian Abel, András Benczúr, Daniel Kohlsdorf, Martha Larson, Róbert Pálovics

Multimedia Computing

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge.

Original language	English
Title of host publication	RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems
Editors	S. Sen, W. Geyer
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	425-426
Number of pages	2
ISBN (Electronic)	978-1-4503-4035-9
DOIs	https://doi.org/10.1145/2959100.2959207
Publication status	Published - 2016
Event	10th ACM Conference on Recommender Systems, RecSys 2016 - MIT, Boston, MA, United States Duration: 15 Sept 2016 → 19 Sept 2016 https://recsys.acm.org/recsys16/

Conference

Conference	10th ACM Conference on Recommender Systems, RecSys 2016
Country/Territory	United States
City	Boston, MA
Period	15/09/16 → 19/09/16
Internet address	https://recsys.acm.org/recsys16/

Keywords

Recommender Systems
Data Mining Challenge
XING

Access to Document

10.1145/2959100.2959207

Cite this

@inproceedings{bffb7eb6e6fa4f5ca775177617ee7392,

title = "RecSys Challenge 2016",

abstract = "The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge.",

keywords = "Recommender Systems, Data Mining Challenge, XING",

author = "Fabian Abel and Andr{\'a}s Bencz{\'u}r and Daniel Kohlsdorf and Martha Larson and R{\'o}bert P{\'a}lovics",

year = "2016",

doi = "10.1145/2959100.2959207",

language = "English",

pages = "425--426",

editor = "S. Sen and W. Geyer",

booktitle = "RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "10th ACM Conference on Recommender Systems, RecSys 2016 ; Conference date: 15-09-2016 Through 19-09-2016",

url = "https://recsys.acm.org/recsys16/",

}

Abel, F, Benczúr, A, Kohlsdorf, D, Larson, M & Pálovics, R 2016, RecSys Challenge 2016. in S Sen & W Geyer (eds), RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems. Association for Computing Machinery (ACM), New York, pp. 425-426, 10th ACM Conference on Recommender Systems, RecSys 2016, Boston, MA, United States, 15/09/16. https://doi.org/10.1145/2959100.2959207

RecSys Challenge 2016. / Abel, Fabian; Benczúr, András; Kohlsdorf, Daniel et al.
RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems. ed. / S. Sen; W. Geyer. New York: Association for Computing Machinery (ACM), 2016. p. 425-426.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - RecSys Challenge 2016

AU - Abel, Fabian

AU - Benczúr, András

AU - Kohlsdorf, Daniel

AU - Larson, Martha

AU - Pálovics, Róbert

PY - 2016

Y1 - 2016

N2 - The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge.

AB - The 2016 ACM Recommender Systems Challenge focused on the problem of job recommendations. Given a large dataset from XING that consisted of anonymized user profiles, job postings, and interactions between them, the participating teams had to predict postings that a user will interact with. The challenge ran for four months with 366 registered teams. 119 of those teams actively participated and submitted together 4,232 solutions yielding in an impressive neck-and-neck race that was decided within the last days of the challenge.

KW - Recommender Systems

KW - Data Mining Challenge

KW - XING

U2 - 10.1145/2959100.2959207

DO - 10.1145/2959100.2959207

M3 - Conference contribution

SP - 425

EP - 426

BT - RecSys'16 Proceedings of the 10th ACM Conference on Recommender Systems

A2 - Sen, S.

A2 - Geyer, W.

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - 10th ACM Conference on Recommender Systems, RecSys 2016

Y2 - 15 September 2016 through 19 September 2016

ER -

RecSys Challenge 2016

Abstract

Conference

Keywords

Access to Document

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Cite this