Inferring relevant people from multi-modal user communication activity

Many network services such as email services and online social networking applications offer enhanced capabilities to standard communication services by mining user data. Examples of such services are friend suggestions, marketing recommendations, etc. In this paper, we look at enhancing related people or a simple contacts application by inferring a dynamic set of relevant people for users at any given time. We argue that this problem is non-trivial and different from finding friends and recommendation systems. We present models for influences of multi-modal communication units that determine the relevance score of people. We present our approach along with performance and evaluation details from a deployed service in a real enterprise communication network.