  The rapid development of Internet technology enables human explore the web
and record the traces of online activities. From the analysis of these
large-scale data sets (i.e. traces), we can get insights about dynamic behavior
of human activity. In this letter, the scaling behavior and complexity of human
activity in the e-commerce, such as music, book, and movie rating, are
comprehensively investigated by using detrended fluctuation analysis technique
and multiscale entropy method. Firstly, the interevent time series of rating
behaviors of these three type medias show the similar scaling property with
exponents ranging from 0.53 to 0.58, which implies that the collective
behaviors of rating media follow a process embodying self-similarity and
long-range correlation. Meanwhile, by dividing the users into three groups
based their activities (i.e., rating per unit time), we find that the scaling
exponents of interevent time series in three groups are different. Hence, these
results suggest the stronger long-range correlations exist in these collective
behaviors. Furthermore, their information complexities vary from three groups.
To explain the differences of the collective behaviors restricted to three
groups, we study the dynamic behavior of human activity at individual level,
and find that the dynamic behaviors of a few users have extremely small scaling
exponents associating with long-range anticorrelations. By comparing with the
interevent time distributions of four representative users, we can find that
the bimodal distributions may bring the extraordinary scaling behaviors. These
results of analyzing the online human activity in the e-commerce may not only
provide insights to understand its dynamic behaviors but also be applied to
acquire the potential economic interest.
