| 摘要 |
Media item recommendation is described. In one example, a statistical model of media consumption is applied to media session consumption data from a community of users to infer parameters of the model. The model comprises a first probability distribution for each user defining a likelihood of the user having a latent characteristic for a session, and a second probability distribution for each latent characteristic defining a likelihood of a user selecting a media item given the latent characteristic. In another example, the inferred parameters are provided to a recommendation engine arranged to recommend media items. The recommendation engine uses the model with inferred parameters and data describing media items newly consumed by a user to infer a current latent characteristic for a current session of the user, and uses them to generate recommended media items for the user in the current session based on the current latent characteristic.
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