Ice-Breaking: Mitigating cold-start recommendation problem by rating comparison

Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, Jian Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

Recommender system has becomean indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.

Original languageEnglish (US)
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3981-3987
Number of pages7
Volume2015-January
ISBN (Print)9781577357384
StatePublished - 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Other

Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period7/25/157/31/15

Fingerprint

Recommender systems
Ice
Collaborative filtering
Factorization
Calibration

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Xu, J., Yao, Y., Tong, H., Tao, X., & Lu, J. (2015). Ice-Breaking: Mitigating cold-start recommendation problem by rating comparison. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 3981-3987). International Joint Conferences on Artificial Intelligence.

Ice-Breaking : Mitigating cold-start recommendation problem by rating comparison. / Xu, Jingwei; Yao, Yuan; Tong, Hanghang; Tao, Xianping; Lu, Jian.

IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. p. 3981-3987.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xu, J, Yao, Y, Tong, H, Tao, X & Lu, J 2015, Ice-Breaking: Mitigating cold-start recommendation problem by rating comparison. in IJCAI International Joint Conference on Artificial Intelligence. vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 3981-3987, 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 7/25/15.
Xu J, Yao Y, Tong H, Tao X, Lu J. Ice-Breaking: Mitigating cold-start recommendation problem by rating comparison. In IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January. International Joint Conferences on Artificial Intelligence. 2015. p. 3981-3987
Xu, Jingwei ; Yao, Yuan ; Tong, Hanghang ; Tao, Xianping ; Lu, Jian. / Ice-Breaking : Mitigating cold-start recommendation problem by rating comparison. IJCAI International Joint Conference on Artificial Intelligence. Vol. 2015-January International Joint Conferences on Artificial Intelligence, 2015. pp. 3981-3987
@inproceedings{6dffd57423d24bd9a640b8f3221fa4df,
title = "Ice-Breaking: Mitigating cold-start recommendation problem by rating comparison",
abstract = "Recommender system has becomean indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.",
author = "Jingwei Xu and Yuan Yao and Hanghang Tong and Xianping Tao and Jian Lu",
year = "2015",
language = "English (US)",
isbn = "9781577357384",
volume = "2015-January",
pages = "3981--3987",
booktitle = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",

}

TY - GEN

T1 - Ice-Breaking

T2 - Mitigating cold-start recommendation problem by rating comparison

AU - Xu, Jingwei

AU - Yao, Yuan

AU - Tong, Hanghang

AU - Tao, Xianping

AU - Lu, Jian

PY - 2015

Y1 - 2015

N2 - Recommender system has becomean indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.

AB - Recommender system has becomean indispensable component in many e-commerce sites. One major challenge that largely remains open is the cold-start problem, which can be viewed as an ice barrier that keeps the cold-start users/items from the warm ones. In this paper, we propose a novel rating comparison strategy (RAPARE) to break this ice barrier. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and warm users/items. We instantiate our RAPARE strategy on the prevalent method in recommender system, i.e., the matrix factorization based collaborative filtering. Experimental evaluations on two real data sets validate the superiority of our approach over the existing methods in cold-start scenarios.

UR - http://www.scopus.com/inward/record.url?scp=84949789215&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949789215&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84949789215

SN - 9781577357384

VL - 2015-January

SP - 3981

EP - 3987

BT - IJCAI International Joint Conference on Artificial Intelligence

PB - International Joint Conferences on Artificial Intelligence

ER -