Patent ID: 8583674

Claim:
A computer-implemented method of recommending one or more media items, comprising: receiving a data set comprising data describing media session consumption for a plurality of users, each media session comprising at least one media item; loading a statistical model of media consumption, the statistical model comprising: a first probability distribution for each user of the plurality of users defining a likelihood of a respective user having one of a plurality of latent characteristics for a given media session, wherein each latent characteristic of the plurality of latent characteristics is associated with an affinity of the plurality of users for consuming related media within a substantially short period of time; and a second probability distribution for each latent characteristic of the plurality of latent characteristics defining a likelihood of one or more of the plurality of users selecting a particular media item based on a respective latent characteristic; applying the data set to the statistical model to infer parameters of the first and second probability distributions; and providing the parameters to a recommendation engine configured to use the statistical model and the inferred parameters to recommend one or more media items to a selected user, the one of a plurality of latent characteristics relating to an underlying mood of the respective user during a given media session and the underlying mood being associated with the affinity of the plurality of users for consuming related media within a threshold time interval.