On targeting Markov segments
Moses Charikar, Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew Tomkins · 1999
Consider two user populations, of which one is targered and the other is not.Users in the targeted population follow a Markov chain on a space of n states.The untargeted population follows another Markov chain, also defined on the same set of n states.Each time a user arrives at a state, he/she is presented with information appropriate for the targeted population (an advertisement, or a recommendation) with some probability.Presenting the advertisement incurs a cost.Notice that while the revenue grows in proportion to the flow of targeted users through the state, the cost grows in proportion to the total flow (targeted and untargeted) through the state.How can we compute the best advertisement policy?The world-wide web is a natural setting for such a problem.Internet service providers have trail information for building such Markovian user models where states correspond to pages on the web.In this paper we study the simple problem above, as well as the variants with multiple targetable segments.In some settings the policy need not be a static probability distribution on states.Instead, we can dynamically vary the policy based on the user's path through the states.