Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation

Sahebi, S and Brusilovsky, P (2013) Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation. In: UNSPECIFIED.

[img]
Preview
PDF
Available under License : See the attached license file.

Download (504kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it. © 2013 Springer-Verlag.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sahebi, Sshs106@pitt.eduSHS106
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 26 September 2013
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 7899 L
Page Range: 289 - 295
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-38844-6_25
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9783642388439
ISSN: 0302-9743
Date Deposited: 19 Jun 2014 14:43
Last Modified: 08 Dec 2017 14:55
URI: http://d-scholarship.pitt.edu/id/eprint/21917

Metrics

Monthly Views for the past 3 years

Plum Analytics

Altmetric.com


Actions (login required)

View Item View Item