Google Inc.

Yu He

Kun Zhang

David P Stoutamire

Cristos Jon Goodrow

Kevin Patrick Murphy

Li Wei

Xinmei Cai

Hao He

Aditee Kumthekar

Daniel Joseph Clancy

Zhihui Chen

Jonathan Frankle

Varun Kacholia

This disclosure relates to adaptive recommendations for user-generated mediasets. A mediaset component provides for users to generate mediasets. A user-generated mediaset can include a user-generated playlist or a user-generated media channel. A monitoring component monitors consumption of media, e.g., by a consumer. A relatedness component determines a set of the user-generated mediasets that are related to the media consumed by the consumer. A recommendation component recommends a subset of the user-generated mediasets based on a set of criteria. A rights management component determines a set of authorizations of the consumer for respective media content associated with the set of user-generated mediasets, and takes at least one action based on the set of authorizations, e.g., updating one of the mediasets based on the set of authorizations.

Adaptive recommendations of user-generated mediasets

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.

Video recommendation based on video co-occurrence statistics

Mechanisms are provided that: identify topics associated with a plurality of pieces of media content presented in a session; calculate a distance metric for pairs of topics, wherein each of the pairs of topics includes a first topic associated with a first piece of media content and a second topic associated with a second piece of media content, and wherein the second piece of media content was presented within a given span of the presentation of the first piece of media content; for each first topic of the pairs of topics, generate a rank-ordered list for all corresponding second topics; for each of the plurality of pieces of media content, generate a single rank-ordered list of all second topics; and for each of the plurality of pieces of media content, identify one or more other pieces of media content as recommended media content based on the single rank-ordered list.

Methods, systems, and media for recommending media content

Systems and methods for segmenting a video based on user engagement in respective segments of the video are presented. In one or more aspects, a system is provided that includes an engagement component configured to receive information regarding respective engagement of a plurality of users in connection with respective segments of a video. The system further includes an analysis component configured to analyze the information and calculate user engagement scores for the respective video segments, wherein the user engagement scores reflect level of the plurality of users' interest regarding the respective video segments, and an identification component configured to identify a subset of the video segments associated with relatively higher user engagement scores in comparison to other video segments.

Segmentation of a video based on user engagement in respective segments of the video

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for clustering videos and using video clusters to present content. One method includes generating target video-specific clusters of co-watched videos for target videos, according to keywords for each target video, and storing data associating the target video with its clusters. The clusters can then be used in various applications, including identifying related videos, augmenting video search results, re-ordering video search results, and identifying content for a target video.

Target-video specific co-watched video clusters

Methods and systems to locate related videos in a video access log. In one embodiment, the method comprises extracting user events from a log database, tagging each event as positive or negative, determining if a video is positively interacted and processing the tagged videos in a sliding window to determine positive interactions between a pair of videos.

User interaction based related videos

Methods, system, and media for recommending media content

Methods and systems to locate related digital content items in a content access log. In one embodiment, the method comprises extracting user events from a content access log, tagging each event as positive or negative, determining if a content item is positively interacted and processing the tagged items in a sliding window to determine positive interactions between a pair of content items.

User interaction based related digital content items

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing video searches using query-independent quality scores.

Query independent quality signals

Recommendations for a media item associated with a primary entity are based on co-interaction information gathered from other media content items of several different media types that are also associated with the primary entity. Co-interaction information can include, for example, co-click data for websites, co-watch data for videos, or co-purchase data for purchases. The co-interaction data is processed to determine a co-interaction score between primary media items and secondary media items. From the co-interaction scores, secondary entities associated with the secondary media items are determined. A relatedness score is determined for these secondary entities based on the aggregation of the co-interaction scores of the secondary media items they are associated with. The relatedness score indicates a determination of how related one entity is to another. The secondary entities are ranked according to relatedness score in order to determine secondary entities most relevant to the primary entity.

Growing community of inventors

Sunnyvale, CA, United States of America

Yu He