Information Interaction Profile of Choice Adoption

TL;DR

This paper presents a convex inference method to uncover and visualize the interaction networks of information entities, revealing how combined exposures influence user actions more than individual effects across real-world datasets.

Contribution

It introduces a novel convex model for inferring dynamic interaction profiles of information entities, advancing understanding of complex interaction mechanisms in user behavior.

Findings

Outperforms baseline models in real-world datasets

Recovers state-of-the-art interaction process results

Provides intuitive visualization of interaction profiles

Abstract

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.