Passive Profiling from Server Logs in an Online Recruitment Environment

Abstract

The success of recommender systems ultimately depends on the availability of comprehensive user profiles that accurately capture the interests of endusers. However, the automatic compilation of such profiles represents a complex learning task. In this paper, we focus on how accurate user profiles can be generated directly from analysing the behaviours of Web users in the CASPER project. In CASPER user profiles are constructed by passively monitoring the click-stream and read-time behaviour of users. We will argue that building accurate profiles from such data is far from straightforward. In particular, we will describe the techniques that are used in CASPER to generate highquality graded user profiles from server logs. Finally, we will describe a comparative evaluation of these different techniques on real user data.