  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.
