Google Inc.

Demandtec, Inc.

Cristos Jon Goodrow

Kun Zhang

Yu He

Daniel Joseph Clancy

Fei Xiao

Jeffrey B Winner

Krishna Venkatraman

Rob Parkin

Jay Robert Girotto

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 learning module of an information retrieval system is configured to automatically learn distinctive characteristics used by different web sites when presenting data variables of interest. The learned information can then be used to identify data variables of interest on arbitrary web pages of the web sites. In one embodiment, the learning process is guided by feeds provided by the web sites that list values for data variables of interest, and by web pages also provided by the web sites. The values of the feeds enable the learning module to identify candidate portions of the web pages that may represent a data variable of interest. Weights are computed for different values of various properties of the candidate portions, aggregated over all the analyzed pages, and used to identify one of the candidate portions as being the best candidates.

Learning characteristics for extraction of information from web pages

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.

Cross media type recommendations for media items based on identified entities

A computer implemented method and system for providing price updates is provided. Data is received by a system. The received data is filtered by the system to identify triggers in the received data. Actions related to identified triggers are performed by the system. Business rule compliance is performed according to a business rule hierarchy. Updated prices are provided.

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Burlingame, CA, United States of America

Cristos Jon Goodrow