System and Method for Content-Based Media Analysis

A computer-implemented method for content-based media analysis, the method comprising:

• generating a different media vector for each media item of a plurality of media items based on a synopsis of the media item, the generating comprising:
• generating a word vector for each word of a plurality of words in the synopsis of the media item, thereby generating a plurality of word vectors;
• combining the plurality of word vectors into a mean vector for the media item, the mean vector being the media vector;
• and
• storing the mean vector, in a memory, as the media vector associated with the media item;
• identifying a target media item, the target media item associated with a seed media vector;
• determining R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items;
• clustering the R nearest neighbors for the target media item into K clusters;
• and
• selecting a second plurality of media items for recommendation to a user based on the K clusters.

A non-transitory machine-readable medium storing processor-executable instructions which, when executed by a processor, cause the processor to:

• generate a different media vector for each media item of a plurality of media items based on a synopsis of the media item, the generating comprising:
• generating a word vector for each word of a plurality of words in the synopsis of the media item, thereby generating a plurality of word vectors;
• combining the plurality of word vectors into a mean vector for the media item, the mean vector being the media vector;
• and
• storing the mean vector, in a memory, as the media vector associated with the media item;
• identify a target media item, the target media item is associated with a seed media vector;
• determine R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items;
• cluster the R nearest neighbors for the target media item into K clusters;
• and
• select a second plurality of media items for recommendation to a user based on the K clusters.