Expert recommendation based on social drivers, social network analysis, and semantic data representation

Maryam Fazel-Zarandi*, Hugh J. Devlin, Yun Huang, Noshir Contractor

*Corresponding author for this work

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

38 Scopus citations

Abstract

Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals'motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users'motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users'motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2011 - Held at the 5th ACM Conference on Recommender Systems, RecSys 2011
Pages41-48
Number of pages8
DOIs
StatePublished - 2011
Event2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2011 - Held at the 5th ACM Conference on Recommender Systems, RecSys 2011 - Chicago, IL, United States
Duration: Oct 27 2011Oct 27 2011

Publication series

NameProceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2011 - Held at the 5th ACM Conference on Recommender Systems, RecSys 2011

Other

Other2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2011 - Held at the 5th ACM Conference on Recommender Systems, RecSys 2011
Country/TerritoryUnited States
CityChicago, IL
Period10/27/1110/27/11

ASJC Scopus subject areas

  • Information Systems

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