Patent ID: 8589228

Claim:
A computer-implemented process for modeling user click-through behaviors, comprising using a computer to perform process actions for: receiving one or more databases of historical click-through data derived from one or more groups of users, said historical click-through data comprising click and impression data for URLs in combination with discrete user-specific and URL-specific attributes for each URL; processing the historical click-through data to learn a Bayesian “outer model” of prior Gaussian distributions comprising a set of continuous random variables corresponding to each of the attributes contained in the historical click-through data; constructing a query response page based on the prior Gaussian distributions of the outer model in response to a current query session, and providing that query response page to a user display device; constructing a session-specific probabilistic “inner model” using discrete user-specific and URL-specific attributes derived from the current query session; performing a joint Bayesian inference using the prior Gaussian distributions of the outer model in combination with the inner model and observations of user clicks in the current query session to learn posterior distributions for the current query session; and using the posterior distributions for the current query session, updating any prior Gaussian distributions in the outer model corresponding to the user-specific and URL-specific attributes derived from the current query session.