Enhancing group recommendation by incorporating social relationship interactions

Mike Gartrell, Xinyu Xing, Qin Lv, Aaron Beach, Richard Han, Shivakant Mishra, Karim Seada

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

124 Citations (Scopus)

Abstract

Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

Original languageEnglish (US)
Title of host publicationProceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10
Pages97-106
Number of pages10
DOIs
StatePublished - Dec 1 2010
Event16th ACM International Conference on Supporting Group Work, GROUP'10 - Sanibel Island, FL, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameProceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10

Other

Other16th ACM International Conference on Supporting Group Work, GROUP'10
CountryUnited States
CitySanibel Island, FL
Period11/7/1011/10/10

Fingerprint

Recommender systems

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

Cite this

Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., & Seada, K. (2010). Enhancing group recommendation by incorporating social relationship interactions. In Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10 (pp. 97-106). (Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10). https://doi.org/10.1145/1880071.1880087
Gartrell, Mike ; Xing, Xinyu ; Lv, Qin ; Beach, Aaron ; Han, Richard ; Mishra, Shivakant ; Seada, Karim. / Enhancing group recommendation by incorporating social relationship interactions. Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10. 2010. pp. 97-106 (Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10).
@inproceedings{353a14e24ad84d42859dd896fd8a1f72,
title = "Enhancing group recommendation by incorporating social relationship interactions",
abstract = "Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.",
author = "Mike Gartrell and Xinyu Xing and Qin Lv and Aaron Beach and Richard Han and Shivakant Mishra and Karim Seada",
year = "2010",
month = "12",
day = "1",
doi = "10.1145/1880071.1880087",
language = "English (US)",
isbn = "9781450303873",
series = "Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10",
pages = "97--106",
booktitle = "Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10",

}

Gartrell, M, Xing, X, Lv, Q, Beach, A, Han, R, Mishra, S & Seada, K 2010, Enhancing group recommendation by incorporating social relationship interactions. in Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10. Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10, pp. 97-106, 16th ACM International Conference on Supporting Group Work, GROUP'10, Sanibel Island, FL, United States, 11/7/10. https://doi.org/10.1145/1880071.1880087

Enhancing group recommendation by incorporating social relationship interactions. / Gartrell, Mike; Xing, Xinyu; Lv, Qin; Beach, Aaron; Han, Richard; Mishra, Shivakant; Seada, Karim.

Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10. 2010. p. 97-106 (Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10).

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

TY - GEN

T1 - Enhancing group recommendation by incorporating social relationship interactions

AU - Gartrell, Mike

AU - Xing, Xinyu

AU - Lv, Qin

AU - Beach, Aaron

AU - Han, Richard

AU - Mishra, Shivakant

AU - Seada, Karim

PY - 2010/12/1

Y1 - 2010/12/1

N2 - Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

AB - Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming sessions for coworkers and social TV for family members or friends. Group recommendation is a challenging problem due to the dynamics of group memberships and diversity of group members. Previous work focused mainly on the content interests of group members and ignored the social characteristics within a group, resulting in suboptimal group recommendation performance. In this work, we propose a group recommendation method that utilizes both social and content interests of group members. We study the key characteristics of groups and propose (1) a group consensus function that captures the social, expertise, and interest dissimilarity among multiple group members; and (2) a generic framework that automatically analyzes group characteristics and constructs the corresponding group consensus function. Detailed user studies of diverse groups demonstrate the effectiveness of the proposed techniques, and the importance of incorporating both social and content interests in group recommender systems.

UR - http://www.scopus.com/inward/record.url?scp=78751690625&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78751690625&partnerID=8YFLogxK

U2 - 10.1145/1880071.1880087

DO - 10.1145/1880071.1880087

M3 - Conference contribution

AN - SCOPUS:78751690625

SN - 9781450303873

T3 - Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10

SP - 97

EP - 106

BT - Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10

ER -

Gartrell M, Xing X, Lv Q, Beach A, Han R, Mishra S et al. Enhancing group recommendation by incorporating social relationship interactions. In Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10. 2010. p. 97-106. (Proceedings of the 16th ACM International Conference on Supporting Group Work, GROUP'10). https://doi.org/10.1145/1880071.1880087