Abstract: In some embodiments, a method determines a reward metric based on feedback for an instance of content. A delivery status for a delivery constraint of the instance of content is applied to the reward metric to generate a constrained reward metric. The method uses the constrained reward metric to train a model. The model is used to select from a plurality of instances of content. One of the plurality of instances of content is selected for delivery using the model.

Abstract: In some embodiments, a method receives information for a delivery of instances of supplemental content for a plurality of line items. A line item is associated with an instance of supplemental content that can be delivered and a pacing curve that describes a pace of delivery over time. The method updates a parameter for the line item to generate an updated parameter based on the delivery of the instances of supplemental content and a desired pacing behavior. The updated parameter is provided to a selection system that uses the updated parameter to select an instance for delivery. The delivery of instances of supplemental content for the line item is adjusted to meet the pacing curve based on a characteristic of the pacing behavior.

Abstract: In some embodiments, a method receives information for a delivery of instances of supplemental content for a plurality of line items. A line item is associated with an instance of supplemental content that can be delivered and a pacing curve that describes a pace of delivery over time. The method updates a parameter for the line item to generate an updated parameter based on the delivery of the instances of supplemental content and a desired pacing behavior. The updated parameter is provided to a selection system that uses the updated parameter to select an instance for delivery. The delivery of instances of supplemental content for the line item is adjusted to meet the pacing curve based on a characteristic of the pacing behavior.

Abstract: In some embodiments, a method determines a reward metric based on feedback for an instance of content. A delivery status for a delivery constraint of the instance of content is applied to the reward metric to generate a constrained reward metric. The method uses the constrained reward metric to train a model. The model is used to select from a plurality of instances of content. One of the plurality of instances of content is selected for delivery using the model.

Abstract: In some embodiments, a method inputs a sequence of historical behaviors for a plurality of instances of content into a prediction network to generate a sequence of values that model the sequence of historical behaviors. A restriction on an operation performed by the prediction network is based on a characteristic of an viewing behavior. A sequence of attention scores is generated based on a similarity of a current behavior for a first instance of content to respective instances of historical behaviors in the sequence of historical behaviors. The method adjusts respective values based on corresponding attention scores to generate an adjusted sequence of values. The adjusted sequence of features are sampled to generate an output from the prediction network that models the sequence of historical behaviors based on the current behavior. The output for determining a prediction if the current behavior is indicative of the viewing behavior.

Abstract: In some embodiments, a method receives a request for supplemental content to be provided in association with main content. The method selects an instance of supplemental content based on a long-term reward metric and a short-term reward metric. The long-term reward metric is based on feedback from delivery of a plurality of instances of supplemental content and a delivery status for a delivery constraint of one instance of supplemental content. The short-term reward metric is based on feedback from delivery of the one instance of supplemental content. The long-term reward metric is based on feedback from delivery of a plurality of instances of supplemental content and the short-term reward metric is based on feedback from delivery of one instance of supplemental content. The instance of supplemental content is provided to a client device.

Abstract: In some embodiments, a method inputs a sequence of historical behaviors for a plurality of instances of content into a prediction network to generate a sequence of values that model the sequence of historical behaviors. A restriction on an operation performed by the prediction network is based on a characteristic of an viewing behavior. A sequence of attention scores is generated based on a similarity of a current behavior for a first instance of content to respective instances of historical behaviors in the sequence of historical behaviors. The method adjusts respective values based on corresponding attention scores to generate an adjusted sequence of values. The adjusted sequence of features are sampled to generate an output from the prediction network that models the sequence of historical behaviors based on the current behavior. The output for determining a prediction if the current behavior is indicative of the viewing behavior.

Abstract: In some embodiments, a method receives a request for supplemental content to be provided in association with main content. The method selects an instance of supplemental content based on a long-term reward metric and a short-term reward metric. The long-term reward metric is based on feedback from delivery of a plurality of instances of supplemental content and a delivery status for a delivery constraint of one instance of supplemental content. The short-term reward metric is based on feedback from delivery of the one instance of supplemental content. The long-term reward metric is based on feedback from delivery of a plurality of instances of supplemental content and the short-term reward metric is based on feedback from delivery of one instance of supplemental content. The instance of supplemental content is provided to a client device.

Abstract: Embodiments of the present invention provide a method for learning a media access control address, a network device and a system. The method includes: generating a key value according to a key field of a received packet, and obtaining an index value corresponding to the key value according to the key value; performing a linear random iteration on the index value according to a preset number of iterations; searching, according to an iteration index value obtained during a first iteration, in a MAC address pool for a MAC address corresponding to the iteration index value obtained during the first iteration; and learning a MAC address of the packet according to a search result. By using the embodiments of the present invention a media access control address space may be fully utilized, and the problem of media access control address conflicts may be solved to the greatest extent.

Abstract: Embodiments of the present invention provide a method for learning a media access control address, a network device and a system. The method includes: generating a key value according to a key field of a received packet, and obtaining an index value corresponding to the key value according to the key value; performing a linear random iteration on the index value according to a preset number of iterations; searching, according to an iteration index value obtained during a first iteration, in a MAC address pool for a MAC address corresponding to the iteration index value obtained during the first iteration; and learning a MAC address of the packet according to a search result. By using the embodiments of the present invention a media access control address space may be fully utilized, and the problem of media access control address conflicts may be solved to the greatest extent.

Abstract: A packet forwarding method, a packet forwarding device, and a packet forwarding system are provided. The method includes: acquiring, by a first board, a source media access control (MAC) address and an input port number in a packet, and looking up a MAC address table according to the source MAC address; learning the source MAC address if no same entry is found; and looking up a forwarding policy table according to the input port number to acquire a forwarding policy, and sending the packet to a second board according to the forwarding policy, so that the second board learns the source MAC address to realize synchronization of MAC addresses. The synchronization of MAC addresses is realized by acquiring the forwarding policy without changing a current forwarding process or influencing an execution efficiency of a central processing unit (CPU).

Abstract: A packet forwarding method, a packet forwarding device, and a packet forwarding system are provided. The method includes: acquiring, by a first board, a source media access control (MAC) address and an input port number in a packet, and looking up a MAC address table according to the source MAC address; learning the source MAC address if no same entry is found; and looking up a forwarding policy table according to the input port number to acquire a forwarding policy, and sending the packet to a second board according to the forwarding policy, so that the second board learns the source MAC address to realize synchronization of MAC addresses. The synchronization of MAC addresses is realized by acquiring the forwarding policy without changing a current forwarding process or influencing an execution efficiency of a central processing unit (CPU).