EPE: An Embedded Personalization Engine for Mobile Users

Abstract

The proposed embedded personalization engine (EPE) utilizes valuable in-device usage data to infer mobile user interests in a privacy-preserving manner. To provide users with personalized services, the proposed approach analyzes both the usage data inside a mobile device and service items - such as news articles and mobile apps - using the Open Directory Project (ODP) as a knowledge base. Embedded classification and ranking methodologies effectively match service items with inferred user interests. The scenario-based evaluation shows that EPE gives users highly personalized services with both reasonable perceived latency and little energy consumption.