Evolving the User Graph: From unsupervised topic models to knowledge assisted networks

Abstract

The next generation intelligent devices need to understand and evolve with the user. Towards this goal, we present a User Graph generation framework that models user's level of interest and knowledge across a set of categories. The user graph is built through an unsupervised and semi-supervised topic modeling process, using latent semantic analysis technology. The self-evolving framework utilizes in-device user data, is built and managed within a local mobile device, thereby ensuring user privacy without the need for additional network based infrastructure. We present and analyze our trial results, aimed at optimizing model accuracy and execution efficiency. In addition to native application adaptation use cases, we also present three new services: Graph Clusters, Graph Shares and Graph Nets that utilize the framework.