Abstract:
Systems and methods for cold-start and continuous-learning via evolutionary explorations are provided. The system includes a database including serving data. A computer server is in communication with the database, the computer server is programmed to: obtain an advertisement opportunity including user data and page data; extract semantic features from the user data, the page data, and a campaign; determine a score that measures a similarity between the advertisement opportunity and the campaign using the semantic features; assign a set of weights to the semantic features when determining the score during a first time period; collect click data on the campaign while using the set of weights to run the campaign in the first time period; update the set of weights using the click data by minimizing a logistic loss function; and assign an updated set of weights to the semantic features during a second time period.
Abstract:
A computer system that implements a method for optimizing an ad campaign may be configured to receive an online ad display request to display an ad to a viewer and obtain at least three probabilities—a first probability that the ad will receive a positive response from the viewer, a second probability that the ad will receive a neutral response from the viewer, and a third probability that the ad will receive a negative response from the viewer. Additionally, the computer system may also be configured to determine a gain value of displaying the ad; and determine a bidding price associated with the ad request based on the gain value.