Abstract:
A system and method provides video recommendations for a target video in a video sharing environment. The system selects one or more videos that are on one or more video playlists together with the target video. The video co-occurrence data of the target video associates the target video and another video on one or more same video playlists and frequency of the target video and another video on the video playlists is computed. Based on the video co-occurrence data of the target video, one or more co-occurrence videos are selected and ranked based on the video co-occurrence data of the target video. The system selects one or more videos from the co-occurrence videos as video recommendations for the target video.
Abstract:
A system receives a user request for a media item and identifies candidate media items for suggesting to the user with the media item. The system predicts a user consumption time for each candidate media item and selects a sub-set of the candidate media items that have higher predicted user consumption times. The system provides the requested media item with the sub-set of the candidate media items.
Abstract:
A system and method provides video recommendations for a target video in a video sharing environment. The system selects one or more videos that are on one or more video playlists together with the target video. The video co-occurrence data of the target video associates the target video and another video on one or more same video playlists and frequency of the target video and another video on the video playlists is computed. Based on the video co-occurrence data of the target video, one or more co-occurrence videos are selected and ranked based on the video co-occurrence data of the target video. The system selects one or more videos from the co-occurrence videos as video recommendations for the target video.
Abstract:
A system and method provides video recommendations for a target video in a video sharing environment. The system selects one or more videos that are on one or more video playlists together with the target video. The video co-occurrence data of the target video associates the target video and another video on one or more same video playlists and frequency of the target video and another video on the video playlists is computed. Based on the video co-occurrence data of the target video, one or more co-occurrence videos are selected and ranked based on the video co-occurrence data of the target video. The system selects one or more videos from the co-occurrence videos as video recommendations for the target video.
Abstract:
A system and computer program product are provided for improving the utility of video recommendations in a content system via de-duplication of highly similar thumbnail images. For each video added to an online content system, a thumbnail image is generated and stored. For each such thumbnail image a compressed representation is computed. During playback of a video, a set of related videos is generated. For each video in the set, the corresponding thumbnail image and its compressed representation are retrieved. A measure of visual distance is computed for each pair in the set of representations, and measures indicating excess similarity are identified. Similarity is reduced via selective removal of some of the representations. An identification of the thumbnail images and videos corresponding to the remaining representations is produced.