  User behaviour targeting is essential in online advertising. Compared with
sponsored search keyword targeting and contextual advertising page content
targeting, user behaviour targeting builds users' interest profiles via
tracking their online behaviour and then delivers the relevant ads according to
each user's interest, which leads to higher targeting accuracy and thus more
improved advertising performance. The current user profiling methods include
building keywords and topic tags or mapping users onto a hierarchical taxonomy.
However, to our knowledge, there is no previous work that explicitly
investigates the user online visits similarity and incorporates such similarity
into their ad response prediction. In this work, we propose a general framework
which learns the user profiles based on their online browsing behaviour, and
transfers the learned knowledge onto prediction of their ad response.
Technically, we propose a transfer learning model based on the probabilistic
latent factor graphic models, where the users' ad response profiles are
generated from their online browsing profiles. The large-scale experiments
based on real-world data demonstrate significant improvement of our solution
over some strong baselines.
