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 fat-tailed (possibly power-law) distributions 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 fat-tailed distributions. 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.

Understanding the dynamics of human movements is key to issues of significant current interest such as behavioral prediction, recommendation, and control of epidemic spreading. We collect and analyze big data sets of human movements in both cyberspace (through browsing of websites) and physical space (through mobile towers) and find a superlinear scaling relation between the mean frequency of visit f and its fluctuation σ : σ ∼ f β with β ≈ 1.2. The probability distribution of the visiting frequency is found to be a stretched exponential function. We develop a model incorporating two essential ingredients, preferential return and exploration, and show that these are necessary for generating the scaling relation extracted from real data. A striking finding is that human movements in cyberspace and physical space are strongly correlated, indicating a distinctive behavioral identifying characteristic and implying that the behaviors in one space can be used to predict those in the other. Traditionally, human movements are restricted to the real physical space (or geospace). Pioneering works demonstrated that there are intrinsic patterns underlying human mobility in physical space [1][2][3], which are key to deciphering the dynamics of human behaviors with wide applications ranging from traffic forecasting [4] to epidemic prevention [5]. Triggered by the tremendous advances in modern information and communication technologies, at present as well as in the future, human movements occur not only in physical space but also in virtual or cyberspace. Here movements in cyberspace are defined broadly as changes in online activities, typically corresponding to switchings in the websites of exploration. Examples of cyberspace movements include World Wide Web surfing along hyperlinks and continuous shopping from commercial websites in a single online session. Do human movements in cyberspace and physical space share common features? Are there general scaling relations underlying human movements in both spaces?Studies of human behaviors have been greatly facilitated by the ubiquity of massive empirical data sets (big data sets) that typically record individuals' movements on various temporal and spatial scales [6,7]. For example, great insights into the dynamics of human movements in physical space were gained by tracking and analyzing the dispersal of dollar bills [1] and through mobile phone [2] and GPS [8] data. There were also efforts to uncover human movements in cyberspace during web surfing [9][10][11] and to probe into human interests dynamics unfolded during cyberspace shopping and browsing [12].In this paper we analyze data sets that record mobile phone users' visits to websites in cyberspace and to mobile towers in the physical space simultaneously and search for a correlation between the movements and general scaling relations. Distinguished from existing approaches to humanmobility analysis [1-3], we focus on the relationship between * ying-cheng.lai@asu.edu flux and fluctuations [13][14][15][16][17][18][19]. In pa...

Temporal bursts are widely observed in many human-activated systems, which may result from both endogenous mechanisms like the highest-priority-first protocol and exogenous factors like the seasonality of activities. To distinguish the effects from different mechanisms is thus of theoretical significance. This letter reports a new timing method by using a relative clock, namely the time length between two consecutive events of an agent is counted as the number of other agents' events appeared during this interval. We propose a model, in which agents act either in a constant rate or with a power-law inter-event time distribution, and the global activity either keeps unchanged or varies periodically vs. time. Our analysis shows that the bursts caused by the heterogeneity of global activity can be eliminated by setting the relative clock, yet the bursts from real individual behaviors still exist. We perform extensive experiments on four large-scale systems, the search engine by AOL, a social bookmarking system -Delicious, a short-message communication network, and a microblogging system -Twitter. Seasonality of global activity is observed, yet the bursts cannot be eliminated by using the relative clock.