Online serving threshold and delivery policy adjustment

A system comprising:

• one or more server computers coupled to a network and Internet;
• and
• one or more databases coupled to the one or more server computers;
• wherein the one or more server computers are configured for:
• during an offline period, initially determining a set of serving thresholds to be utilized in online advertisement serving, wherein a serving threshold is associated with a minimum anticipated click through rate;
• during an offline period, initially determining a set of delivery policies, wherein a delivery policy is associated with one or more rules relating to serving of advertisements in accordance with required or optimal distribution of advertising inventory across serving opportunities;
• during an online period, receiving and storing by a server coupled to the Internet, category-specific advertising information in one or more data sources;
• during an online period, querying the one or more data sources to determine adjustments based on the stored category-specific advertising information;
• during an online period, and based at least in part on information obtained during an online period, including the stored category-specific advertising information sent from the one or more data sources over the Internet, dynamically adjusting in real-time, based on the determined adjustments, at least one of the set of serving thresholds to determine at least one adjusted serving threshold, and dynamically adjusting in real-time at least one of the set of delivery policies to determine at least one adjusted delivery policy;
• during an online period, utilizing a machine learning-based model in dynamically updating decision-making in real-time with regard to serving of online advertisements in connection with serving opportunities based at least in part on the at least one adjusted serving threshold and the at least one adjusted delivery policy, adjusted dynamically during the online period, wherein the machine learning-based model is initially trained during an offline period with advertising-related information stored in and queried from the one or more data sources and dynamically updated during an online period with real-time adjustments, wherein an online period is a period of active advertisement serving in which the model is utilized;
• and
• during an online period, transmitting an online advertisement over the Internet based on a particular serving opportunity to a user device of a particular user based on the decision-making by the machine learning-based model.

A non-transitory computer readable medium or media containing instructions for executing a method for use in association with an online advertising auction relating to an online advertising marketplace, the method comprising:

• during an offline period, initially determining a set of serving thresholds to be utilized in online advertisement serving, wherein a serving threshold is associated with a minimum anticipated click through rate;
• during an offline period, initially determining a set of delivery policies, wherein a delivery policy is associated with one or more rules relating to serving of advertisements in accordance with required or optimal distribution of advertising inventory across serving opportunities;
• wherein the distribution of advertising inventory across serving opportunities relates to distribution of advertising inventory across serving opportunities associated with different nodes of a hierarchical taxonomy of behavioral targeting categorical nodes;
• during an online period, receiving and storing by a server coupled to the Internet, category-specific advertising information in one or more data sources;
• during an online period, querying the one or more data sources to determine adjustments based on the stored category-specific advertising information;
• during an online period, and based at least in part on information obtained during an online period, including the stored category-specific advertising information sent from the one or more data sources over the Internet, dynamically adjusting in real-time, based on the determined adjustments, at least one of the set of serving thresholds to determine at least one adjusted serving threshold, and dynamically adjusting in real-time at least one of the set of delivery policies to determine at least one adjusted delivery policy;
• wherein the at least one adjusted serving threshold and the at least one adjusted delivery policy are adjusted at least in part to better optimize user targeting or advertising inventory distribution, based at least in part on the information obtained during the period;
• during an online period, utilizing a machine learning-based model in dynamically updating decision-making in real-time with regard to serving of online advertisements in connection with serving opportunities based at least in part on the at least one adjusted serving threshold and the at least one adjusted delivery policy, adjusted dynamically during the online period, wherein the machine learning-based model is initially trained during an offline period with advertising-related information stored in and queried from the one or more data sources and dynamically updated during an online period with real-time adjustments, wherein an online period is a period of active advertisement serving in which the model is utilized;
• wherein the machine-learning based model is trained using information including historical user behavior information;
• and
• during an online period, transmitting an online advertisement over the Internet based on a particular serving opportunity to a user device of a particular user based on the decision-making by the machine learning-based model.