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

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.

Connecting Researchers with Companies for University-Industry Collaboration

“…social relation [10,14,37,39] and quality [31,32] ) are widely considered in expert finding research. Social relation is used to measure the proximity of two entities in the network or to measure the position of a given entity in the network.…”

“…The theoretical foundations of the selected criteria are summarized in Table 1. [10,14,37,39]; Co-authorship [32] University industry collaboration research…”

Spectral clustering for link prediction in social networks with positive and negative links

“…This is in accordance with characteristics of friendship social networks, such as Balance (Friend of a Friend) and Homophily (Birds of a feather) Theories [10]. We used the generator proposed in [34] and created 2 synthetic data sets based on different network sizes n (50000, 100000), by keeping an identical m nodes degree equal to 50 and for both data sets (p is fixed to 0.2).…”

A Comparative Analysis of Educator’s and Peers’ Influence on Students’ Attitude to Mobile Social Media in Distance Learning

“…Research community networks [2] include four technology components such as a controlled vocabulary (eg., the VIVO (literally 'inside the living') Ontology) for data interoperability, an architecture for data integration and sharing (Linked Open Data), applications for collaboration, funding, business intelligence, or administration and rich faculty profile data of publications, grants, classes, affiliations, interests, etc. Research community networks' tools facilitate the development of new collaborations and team science to address new or existing research challenges through the rapid discovery and recommendation of researchers, expertise, and resources [3,6]. Research community networks' tools differ from search engines such as Google in that they access information in databases and other data not limited to web pages.…”