Personalized Application Enablement by Web Session Analysis and Multisource User Profiling

摘要

Mastering knowledge of the user profile is one of the technical cornerstones for service providers who handle a large amount of end user service consumption data and are well positioned to dynamically infer user interest domains. This paper presents a holistic approach to service personalization by offering a means to gather a user's consumption data from different multimedia services, to create and track user profiles in real time, and to monetize these profiles through targeted content or other personalized service offers. We describe a multisource profiling engine that deals with both operator-controlled domains and over-the-top (OTT) applications. In particular, to cover the Web domain, we combine the multisource profiling engine with a deep packet inspection (DPI)-based keyword inference engine characterizing users' Web browsing sessions in terms of the most relevant keywords searched.