Advertisement conversion prediction based on unlabeled data

A non-transitory computer-readable storage medium storing instructions for providing a prediction whether conversions occur for advertisements, the instructions comprising:

• receiving a message indicating that a clicking event occurs for an advertisement rendered on a user computer device;
• in response to the message, assigning a label to the instance of the advertisement as unlabeled;
• generating a set of feature vectors based on data of the advertisement;
• training a machine learning model using the set of feature vectors of the unlabeled instance based on a first training algorithm for the unlabeled advertisement;
• and
• generating, by the machine learning model, a prediction of probability on whether a conversion event occur after a clicking event for a new advertisement, by feeding data of the new advertisement to the machine learning model.

A computing system, comprising:

• a memory for storing parameters of a machine learning model for predicting whether clicking events occur for an advertisement;
• a labeling module configured to label the advertisement as unlabeled, in response to an impression event occurs when the advertisement is rendered on a user device;
• a feature extraction module configured to generate one or more feature vectors based on data of the unlabeled advertisement;
• and
• a training module configured to train the parameters of the machine learning model, without waiting for a clicking event as a result of the advertisement, using the feature vectors of the unlabeled advertisement, by minimizing an objective function including terms for unlabeled advertisements of which impression events have occurred but no clicking events have occurred.