Systems and Methods for Cold-start and Continuous-learning via Evolutionary Explorations

A method, comprising:

• obtaining, by one or more devices having a processor, an existing campaign;
• extracting, by the one or more devices, semantic features from the existing campaign and a new campaign;
• obtaining, by the one or more devices, a semantic similarity between the existing campaign and the new campaign using the semantic features;
• determining, by the one or more devices, a score that combines the semantic similarity and a click through rate (CTR) of the existing campaign;
• and
• selecting, by the one or more devices, an initial set of opportunities at least partially based on the score to cold-start the new campaign.

A non-transitory storage medium configured to store modules comprising:

• module for obtaining obtain an advertisement opportunity comprising user data and page data;
• module for extracting semantic features from the user data, the page data, an existing campaign, and a new campaign;
• module for determining a first similarity between the advertisement opportunity and the new campaign at least partially based on the semantic features from the user data, the page data, and the new campaign;
• module for determining a second similarity between the existing campaign and the new campaign at least partially based on the semantic features from the existing campaign and the new campaign;
• module for determining a score that combines the first similarity and the second similarity;
• and
• module for determining whether to select the advertisement opportunity at least partially based on the score.