  Interactions between pieces of information (entities) play a substantial role
in the way an individual acts on them: adoption of a product, the spread of
news, strategy choice, etc. However, the underlying interaction mechanisms are
often unknown and have been little explored in the literature. We introduce an
efficient method to infer both the entities interaction network and its
evolution according to the temporal distance separating interacting entities;
together, they form the interaction profile. The interaction profile allows
characterizing the mechanisms of the interaction processes. We approach this
problem via a convex model based on recent advances in multi-kernel inference.
We consider an ordered sequence of exposures to entities (URL, ads, situations)
and the actions the user exerts on them (share, click, decision). We study how
users exhibit different behaviors according to combinations of exposures they
have been exposed to. We show that the effect of a combination of exposures on
a user is more than the sum of each exposure's independent effect--there is an
interaction. We reduce this modeling to a non-parametric convex optimization
problem that can be solved in parallel. Our method recovers state-of-the-art
results on interaction processes on three real-world datasets and outperforms
baselines in the inference of the underlying data generation mechanisms.
Finally, we show that interaction profiles can be visualized intuitively,
easing the interpretation of the model.
