User Group based Viewpoint Recommendation using User Attributes for Multiview Videos
Xueting Wang, Yu Enokibori, Takatsugu Hirayama, Kensho Hara, Kenji Mase · 2017
Multiview videos can provide diverse information and high flexibility in enhancing the viewing experience. User-dependent automatic viewpoint recommendation is important for reducing user stress while selecting continually suitable and favorable viewpoints. Existing personal viewpoint recommendation methods have been developed by learning the user»s viewing records. These methods have difficulty in acquiring sufficient personal viewing records in practice. Moreover, they neglect the importance of the user»s attribute information, such as user personality, interest, and experience level in the viewing content. Thus, we propose a group-based recommendation framework consisting of a user grouping approach based on the similarity in existing user viewing records, and a member group estimation approach based on the classification by user attributes. We validate the effectiveness of the proposed group-based recommendation and analyze the relationship between user attributes and multiview viewing patterns.