Inferring Missing Attributes of Users in Large-Scale Social networks

Abstract

User attribute inference plays an important role in personalized recommendation and precision marketing. However, in large-scale social networks, user attributes are often missing. To address the problem, this paper introduces an inference framework for deriving missing attributes of users in largescale social networks. We use Sina Weibo as our experimental platform. The framework leverages various collaborative filtering methods and a similarity learning scheme to infer missing user attribute values. Experimental results demonstrate the proposed framework is able to generate satisfactory inference results.