Emergence of scaling in human-interest dynamics

TL;DR

This paper uncovers power-law scaling in human-interest dynamics using large-scale data, revealing underlying mechanisms like preferential return, inertia, and exploration, and proposes a biased random-walk model to explain these phenomena.

Contribution

It is the first study to analyze and model the scaling behaviors of human-interest dynamics based on big data, introducing a new biased random-walk model.

Findings

Power-law scaling in interest duration, return time, and interest transition.

Identification of three key ingredients: preferential return, inertia, exploration.

Development of a biased random-walk model explaining observed scaling laws.

Abstract

Human behaviors are often driven by human interests. Despite intense recent efforts in exploring the dynamics of human behaviors, little is known about human-interest dynamics, partly due to the extreme difficulty in accessing the human mind from observations. However, the availability of large-scale data, such as those from e-commerce and smart-phone communications, makes it possible to probe into and quantify the dynamics of human interest. Using three prototypical "big data" sets, we investigate the scaling behaviors associated with human-interest dynamics. In particular, from the data sets we uncover power-law scaling associated with the three basic quantities: (1) the length of continuous interest, (2) the return time of visiting certain interest, and (3) interest ranking and transition. We argue that there are three basic ingredients underlying human-interest dynamics: preferential return to previously visited interests, inertial effect, and exploration of new interests. We develop a biased random-walk model, incorporating the three ingredients, to account for the observed power-law scaling relations. Our study represents the first attempt to understand the dynamical processes underlying human interest, which has significant applications in science and engineering, commerce, as well as defense, in terms of specific tasks such as recommendation and human-behavior prediction.