GLORY: Exploration and integration of global and local correlations to improve personalized online social recommendations

Mingxin Gan (), Lily Sun () and Rui Jiang ()
Additional contact information
Mingxin Gan: University of Science and Technology Beijing
Lily Sun: University of Reading
Rui Jiang: Tsinghua University

Information Systems Frontiers, 2019, vol. 21, issue 4, No 12, 925-939

Abstract: Abstract Nowadays people manage their social circles via a variety of online social media which employ social recommendation as an important component. Among social recommendation methods, global methods take an emphasis on common tastes between people while local methods assume that new relations are established mainly through people’s common friends. However, in a real social network, both local and global relations exist, which motivate us to integrate them to improve recommendation performance. To achieve the goal, we proposed a novel hybrid method GLORY to combine global associations with local correlations for social recommendation. GLORY consists of two components: GLOBE and LORY. The former is a globalised regression model to explore the concordance between people’s preference with the relatedness of their friends. The latter is an integration method to fuse global and local correlations via a rigorous statistical model to calibrate the statistical significance of these correlations. Furthermore, we demonstrated the effectiveness of our methods via 10-fold large-scale cross-validation on three real social network datasets (Facebook, Last.fm and Epinions). Results show that GLORY significantly outperform the state-of-the-art methods while LORY is effective across various global and local methods, indicating their promising future for social recommendations.

Keywords: Social recommendations; Social network; Global and local correlations; Regression through the origin; Fisher’s combined probability test (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10796-017-9797-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:21:y:2019:i:4:d:10.1007_s10796-017-9797-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1007/s10796-017-9797-4

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().