Interdisciplinary Matchmaking: Choosing Collaborators by Skill, Acquaintance and Trust

Abstract

Social networks are commonly used to enhance recommender systems. Most of such systems recommend a single resource or a person. However, com- plex problems or projects usually require a team of experts that must work together on a solution. Team recommendation is much more challenging, mostly because of the complex interpersonal relations between members. This chapter presents funda- mental concepts on how to score a team based onmembers’ social context and their suitability for a particular project.We represent the social context of an individual as a three-dimensional social network (3DSN) composed of a knowledge dimension expressing skills, a trust dimension and an acquaintance dimension.Dimensions of a 3DSN are used to mathematically formalize the criteria for prediction of the team’s performance.We use these criteria to formulate the team recommendation problem as amulti-criteria optimization problem.We demonstrate our approach on empirical data crawled from two web2.0 sites: onephoto.net and a social networking site.We construct 3DSNs and analyze properties of team’s performance criteria. 12.1 Introduction Human knowledge grows faster than it ever had. As the total volume of avail