21. A computer readable non-transitory storage medium for tangibly storing thereon computer readable instructions that when executed cause at least one processor to: train a machine-modeled kernel jointly modeling content, semantic information and social network information, the kernel for use with a digital content annotation and recommendation system, the training comprising building the kernel using a content function trained using a training set comprising content feature information, a semantic function trained using semantic feature information of the training data set and a social network function trained using social network feature information of the training data set, an implicit social network determined from a plurality of content items is used in determining the social network feature information used in training the kernel; identify one or more annotations for a test content item other than the plurality of content items used to train the kernel, the identifying using the trained kernel and the test content item's content feature information to identify the one or more annotations; and serving, to a user computing device via an electronic communications network and to a client computing device of a user, the test content item based on the one or more identified annotations and an interest of the user, the serving of the test content item to the user computing device resulting in the test content item being output by the user computing device.
25. The computer readable non-transitory storage medium of claim 21 , the instructions further comprising instructions to: train the content kernel using at least the content feature information associated with each content item of the plurality of content items in the training set; train the semantic kernel using at least the semantic feature information associated with each content item of the plurality of content items in the training set; train the social kernel using at least the social network feature information associated with each content item of the plurality of content items in the training set.