Passive Profiling from Server Logs in an Online Recruitment Environment

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.