Abstract: The present disclosure describes an artificial intelligence approach to digital content recommendation where the recommendation mechanics differ based on the amount of information available. In one aspect, a user is identified as an above-threshold user who has consumed at least a threshold number of digital artifacts or a below-threshold user who has consumed fewer digital artifacts and different recommendation engines are used for above-threshold users and below-threshold users. In another aspect, users are bifurcated into low-data users and high-data users. For high-data users, digital artifacts are directly selected, and for low-data users, digital artifacts are indirectly selected by first selecting a digital artifact property criteria and then selecting digital artifacts that satisfy the selected digital artifact property criteria.

Abstract: One variation of a method includes: receiving a request for advertising content from an ad slot loaded with a webpage rendered by a computing device; accessing a set of ad slot characteristics including an address of the webpage; extracting a set of visual assets, representing advertising content, from a digital advertisement selected for presentation within the ad slot; selecting a presentation layer, in a set of presentation layers, for pairing with the digital advertisement based on the set of visual assets and the set of ad slot characteristics, the presentation layer defining an interactive format and a layout of visual assets within the interactive format; transforming the digital advertisement into an interactive advertisement according to the presentation layer; and rendering the interactive advertisement within the ad slot responsive to an event that moves the ad slot within a viewing window of the computing device.