Team recommendation in open innovation networks

Michele Brocco; Georg Groh

doi:10.1145/1639714.1639789

Team recommendation in open innovation networks

Michele Brocco
, Georg Groh

Informatics 25 - Chair of Data Science and Engineering

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

Open Innovation has become an important new paradigm for incorporating external knowledge and sources in the innovation process of organizations. Besides other discussed arguments the resulting large size of innovator networks suggests that algorithmic approaches for team recommendation may be needed in that scenario. The current work identifies the related difficulties and thoroughly investigates aspects entities for the problem of team recommendation. Based on that, we develop a meta model which allows to instantiate and integrate most of the vast number of the existing socio-/psychological models on optimal team composition. This meta model is necessary for operationalizing our intended team recommendation approach.

Original language	English
Title of host publication	RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems
Pages	365-368
Number of pages	4
DOIs	https://doi.org/10.1145/1639714.1639789
State	Published - 2009
Event	3rd ACM Conference on Recommender Systems, RecSys'09 - New York, NY, United States Duration: 23 Oct 2009 → 25 Oct 2009

Publication series

Name	RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems

Conference

Conference	3rd ACM Conference on Recommender Systems, RecSys'09
Country/Territory	United States
City	New York, NY
Period	23/10/09 → 25/10/09

Keywords

Meta model
Team composition
Team recommendation

Access to Document

10.1145/1639714.1639789

Cite this

@inproceedings{b93a238440c64e1f812eaf4ca116ec98,

title = "Team recommendation in open innovation networks",

abstract = "Open Innovation has become an important new paradigm for incorporating external knowledge and sources in the innovation process of organizations. Besides other discussed arguments the resulting large size of innovator networks suggests that algorithmic approaches for team recommendation may be needed in that scenario. The current work identifies the related difficulties and thoroughly investigates aspects entities for the problem of team recommendation. Based on that, we develop a meta model which allows to instantiate and integrate most of the vast number of the existing socio-/psychological models on optimal team composition. This meta model is necessary for operationalizing our intended team recommendation approach.",

keywords = "Meta model, Team composition, Team recommendation",

author = "Michele Brocco and Georg Groh",

year = "2009",

doi = "10.1145/1639714.1639789",

language = "English",

isbn = "9781605584355",

series = "RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems",

pages = "365--368",

booktitle = "RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems",

note = "3rd ACM Conference on Recommender Systems, RecSys'09 ; Conference date: 23-10-2009 Through 25-10-2009",

}

Brocco, M & Groh, G 2009, Team recommendation in open innovation networks. in RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems. RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 365-368, 3rd ACM Conference on Recommender Systems, RecSys'09, New York, NY, United States, 23/10/09. https://doi.org/10.1145/1639714.1639789

TY - GEN

T1 - Team recommendation in open innovation networks

AU - Brocco, Michele

AU - Groh, Georg

PY - 2009

Y1 - 2009

N2 - Open Innovation has become an important new paradigm for incorporating external knowledge and sources in the innovation process of organizations. Besides other discussed arguments the resulting large size of innovator networks suggests that algorithmic approaches for team recommendation may be needed in that scenario. The current work identifies the related difficulties and thoroughly investigates aspects entities for the problem of team recommendation. Based on that, we develop a meta model which allows to instantiate and integrate most of the vast number of the existing socio-/psychological models on optimal team composition. This meta model is necessary for operationalizing our intended team recommendation approach.

AB - Open Innovation has become an important new paradigm for incorporating external knowledge and sources in the innovation process of organizations. Besides other discussed arguments the resulting large size of innovator networks suggests that algorithmic approaches for team recommendation may be needed in that scenario. The current work identifies the related difficulties and thoroughly investigates aspects entities for the problem of team recommendation. Based on that, we develop a meta model which allows to instantiate and integrate most of the vast number of the existing socio-/psychological models on optimal team composition. This meta model is necessary for operationalizing our intended team recommendation approach.

KW - Meta model

KW - Team composition

KW - Team recommendation

UR - https://www.scopus.com/pages/publications/72249112337

U2 - 10.1145/1639714.1639789

DO - 10.1145/1639714.1639789

M3 - Conference contribution

AN - SCOPUS:72249112337

SN - 9781605584355

T3 - RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems

SP - 365

EP - 368

BT - RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems

T2 - 3rd ACM Conference on Recommender Systems, RecSys'09

Y2 - 23 October 2009 through 25 October 2009

ER -

Team recommendation in open innovation networks

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this