User Segment Identification Based on Similarity in Content Consumption

With the rapid growth of online content consumption, knowing end-users and having actionable content insights has become extremely important for any online content provider. Insights from user segment identification could help in developing a content recommendation as well as new content acquisition. For advertisers, identifying segments could assist in designing ad campaigns with greater target accuracy. In this paper, we propose a new approach of finding user segments based on similarity in content consumption. We have exploited content metadata such as genres for this purpose. However, as many videos have multiple genres, the relative importance of these genres for a movie is not known. To solve this problem, we propose a two-step clustering process. First, we identify movie clusters based on metadata-based similarity. Then, based on these movie clusters, user segments are identified. We also propose a segment based recommendation system. Finally, we demonstrate the effectiveness of our approach through experiments on a large online movie database.