10. A non-transitory computer-readable medium storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform steps 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.
10. A non-transitory computer-readable medium storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform steps 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. 11. The non-transitory computer-readable medium of claim 10 , wherein each statistical model of the plurality of statistical models is a Markov chain.