Deep Relational Factorization Machine Techniques for Content Usage Prediction via Multiple Interaction Types

A non-transitory computer-readable medium having program code stored thereon, the program code executable by a processor to perform operations comprising:

• accessing digital activity data having binary features;
• generating a feature graph representing co-occurrences among the binary features in the digital activity data;
• a step for computing a high-order prediction indicating a probability of an additional digital activity based on the feature graph;
• and
• providing the high-order prediction to a prediction computing system.

A system comprising:

• a deep relational factorization machine comprising:
• a relational feature interaction component for generating a first feature interaction embedding vector and a second feature interaction vector that describe feature interactions between features of digital activity data;
• a graph convolutional neural network (“GCN”) for generating a convolutional combination of the first feature interaction embedding vector and the second feature interaction embedding vector, wherein the convolutional combination describes sample interactions between subsets of the digital activity data;
• and
• an output component configured for generating, from the feature interaction embedding vector and the sample interaction embedding vector, a high-order prediction indicating a probability of an additional digital activity.