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Method and System for Leveraging the Social Graph for People Recommendations

IP.com Disclosure Number: IPCOM000223821D
Publication Date: 2012-Nov-29
Document File: 2 page(s) / 54K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system that leverage social relationships to improve the results of people recommendations systems. Recommending people using this method ensures that not only people with the right skills are applied to a project, but also the people recommended have a better chance of successful collaboration given existing and historical teaming.

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Method and System for Leveraging the Social Graph for People Recommendations

Systems that recommend people to other users (i.e., people recommendation systems) are often inadequate when recommending the most appropriate mix of individuals for a specific project, opportunity, or collaboration need. Current people recommendation systems connect people with people and people with projects in order to facilitate knowledge exchange, collaboration, and project delivery. These systems focus on characterizing each person or project by a set of keywords or topics that best describe them. When these systems apply their matching algorithms, they typically look at each person in isolation, match them against the requirements of the project, and provide a list of recommended people, sometimes referred to as "experts". They then look to compare combinations of skills and choose a subset of people based on the best skills coverage. This approach might produce a set of what looks to be the right skills, but does not necessarily mean that the people being chosen are in fact the best people for the project.

This invention describes a method and system leveraging social relationships to improve people recommendations systems. Recommending people using this method ensures that not only people with the right skills are applied to a project, but also the people recommended have a better chance of successful collaboration given existing and historical teaming.

A random set of highly qualified people does not a good project make, because people are more than the sum of their skills. They are individuals with different personalities, preferences, interests, and colleagues, and it is these apparent intangibles that make a project successful (or not). Through the application of social network analytics (SNA) on the social semantic network (SSN) that describe the people interactions within the business, the invention can capture these intangibles and integrate them into the recommendation system. This allows us to identify which people would work best as a team, taking into account both their skills and their social characteristics.

For example, if two people, John and Jane, have previously worked together on a successful project and their social network is strong, it is reasonabl...