Attribute weighting for media content-based recommendation

摘要

Techniques are disclosed for automatically assigning weights to attributes of media content based in part on how many users actually viewed or listened to the content, as well as how many users “liked” or otherwise indicated a preference for the content. The content items can be any type of audio or visual media content, such as songs, videos, or movies, as well as written content, such as books, articles, journals, advertisements, or magazines. A first similarity score is determined based on a similarity between user preferences for content items. A second similarity score is determined based on a similarity between one or more common attributes of the content items. These attributes are assigned ratings that represent the number of users who consumed the corresponding content. Next, weights are assigned to each of the attributes based on the first and second similarity scores using, for example, linear equation regression techniques.