摘要:
A cost function is stochastically optimized using, e.g., simulated annealing to render a neighborhood of entities based on which content recommendations can be provided to a user of a home entertainment system. The cost function represents a normalized sum of rating similarity scores from entities of the neighborhood that are related to content items viewed by the user.
摘要:
A cost function is stochastically optimized using, e.g., simulated annealing to render a neighborhood of entities based on which content recommendations can be provided to a user of a home entertainment system. The cost function represents a normalized sum of rating similarity scores from entities of the neighborhood that are related to content items viewed by the user.
摘要:
A cost function is stochastically optimized using, e.g., simulated annealing to render a neighborhood of entities based on which content recommendations can be provided to a user of a home entertainment system. The cost function represents a normalized sum of rating similarity scores from entities of the neighborhood that are related to content items viewed by the user.
摘要:
A cost function is stochastically optimized using, e.g., simulated annealing to render a neighborhood of entities based on which content recommendations can be provided to a user of a home entertainment system. The cost function represents a normalized sum of rating similarity scores from entities of the neighborhood that are related to content items viewed by the user.
摘要:
A cost function is stochastically optimized using, e.g., simulated annealing to render a neighborhood of entities based on which content recommendations can be provided to a user of a home entertainment system. The cost function represents a normalized sum of rating similarity scores from entities of the neighborhood that are related to content items viewed by the user.
摘要:
A cost function is stochastically optimized using, e.g., simulated annealing to render a neighborhood of entities based on which content recommendations can be provided to a user of a home entertainment system. The cost function represents a normalized sum of rating similarity scores from entities of the neighborhood that are related to content items viewed by the user.
摘要:
A system and method for effectively supporting content distribution in an electronic network includes a content server and a peer-to-peer network of client devices. The content server stores content items received from a content provider. A recommendation engine of the content server creates a global recommendation list to identify an optimal global candidate from among the stored content items for performing an automatic and transparent content download procedure. The recommendation engine creates the global recommendation list by analyzing selectable content-ranking criteria from a plurality of device users of the client devices. The content server then downloads the optimal global candidate from the stored content items to one or more identified target devices during the content download procedure.
摘要:
A system and method for utilizing account tiers in an electronic network includes a peer-to-peer network of client devices. The client devices are configured to perform content transfers for obtaining desired content items directly over the peer-to-peer network, or from a CDN server device. The client devices generate account-tier selections to choose from among the supported account tiers. A tier manager monitors, updates, and stores the tier selections from the client devices. The tier manager also calculates content prices that vary depending upon the particular selected account tier. The client devices then pay the appropriate designated content prices for accessing and utilizing desired content items.
摘要:
A system and method for effectively supporting content distribution in an electronic network includes a content server and a peer-to-peer network of client devices. The content server stores content items received from a content provider. A recommendation engine of the content server creates a global recommendation list to identify an optimal global candidate from among the stored content items for performing an automatic and transparent content download procedure. The recommendation engine creates the global recommendation list by analyzing selectable content-ranking criteria from a plurality of device users of the client devices. The content server then downloads the optimal global candidate from the stored content items to one or more identified target devices during the content download procedure.