Users on social media sites, such as YouTube, may have dramatically different viewing behaviors, based on a number of factors. Many of these factors are considered by social media recommendation algorithms when identifying videos to recommend to a user. One commonly used algorithm is largely based on co-viewership. This algorithm relies on monitoring the videos that a user watches and identifying a group of other users that have also watched the same or similar video genres. The videos that have been watched by other members of the group, but not watched by the original user, may be selected as a recommendation for the original user with the assumption that the users in the group all have similar interests. This approach generally results in video recommendations that align well with the original users’ interests.

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