Yu He - Sunnyvale CA, US David P. Stoutamire - Redwood City CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 7/00
US Classification:
707727
Abstract:
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.
Hao He - Sunnyvale CA, US Yu He - Sunnyvale CA, US David P. Stoutamire - Redwood City CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 17/00
US Classification:
707791, 707802, 707822, 707828
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a resource's reachability score. In one aspect, a method includes identifying one or more secondary resources reachable through one or more links of a primary resource wherein the secondary resources are within a number of hops from the primary resource; determining an aggregate score for the primary resource based on respective scores of the secondary resources wherein each one of the respective scores is calculated based on prior user interactions with a respective secondary resource; and providing the aggregate score as an input signal to a resource ranking process for the primary resource when the primary resource is represented as a search result responsive to a query.
Cross Media Type Recommendations For Media Items Based On Identified Entities
Daniel J. Clancy - Los Altos CA, US Cristos J. Goodrow - Mountain View CA, US Yu He - Sunnyvale CA, US Kun Zhang - Mountain View CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 17/30
US Classification:
707723, 705 267
Abstract:
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.
User Interaction Based Related Digital Content Items
Yu He - Sunnyvale CA, US David P. Stoutamire - Redwood City CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 7/00
US Classification:
707727
Abstract:
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.
Video Recommendation Based On Video Co-Occurrence Statistics
Li Wei - Milpitas CA, US Kun Zhang - Mountain View CA, US Yu He - Sunnyvale CA, US Xinmei Cai - Tokyo, JP
Assignee:
GOOGLE INC. - Mountain View CA
International Classification:
G06N 5/00
US Classification:
706 54
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.
User Interaction Based Related Digital Content Items
Yu He - Sunnyvale CA, US David Petrie Stoutamire - Redwood City CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 7/00
US Classification:
707727
Abstract:
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.
Adaptive Recommendations Of User-Generated Mediasets
- Mountain View CA, US Yu He - Sunnyvale CA, US Cristos Jon Goodrow - Burlingame CA, US
International Classification:
G06F 16/638 G06F 16/438
Abstract:
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
- Mountain View CA, US Yu He - Sunnyvale CA, US Cristos Jon Goodrow - Burlingame CA, US
International Classification:
G06F 17/30
Abstract:
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.