1. A method comprising: receiving title interaction data, wherein the title interaction data specifies, for each user of a plurality of users, an order in which the user interacted with a plurality of titles; generating a plurality of statistical models, each statistical model of the plurality of statistical models specifying a plurality of probabilities, wherein the plurality of probabilities represent, for each first title of the plurality of titles and each second title of the plurality of titles, a likelihood that a user will interact with the first title then next interact with the second title; wherein generating the plurality of statistical models is performed by creating a global statistical model based on the title interaction data and applying noise to the global statistical model to create each of the plurality of statistical models; refining the plurality of statistical models based on the title interaction data to produce a plurality of refined statistical models; determining a plurality of weight values corresponding to the plurality of refined statistical models for a particular user of the plurality of users, wherein each weight value of the plurality of weight values corresponds to a respective refined statistical model of the plurality of refined statistical models; identifying, for the particular user, one or more recommended titles of the plurality of titles based on the plurality of weight values and the plurality of refined statistical models; wherein the method is performed by one or more computing devices.