Study of the Evolution of Online User Interest Behavior

The click behavior of online users can be regarded as a kind of movement in virtual space, which is usually driven by human interests. Though many breakthroughs have been made in exploring the dynamics of human behavior, little is known about the dynamic evolution of human interest in virtual space, partly due to the extreme difficulty in getting enough behavioral data and accessing the human mind from observations. Based on the large-scale online user behavior data obtained by China Internet Network Information Center, this paper quantifies the browsing behavior of online users from the aspects of moving step length, dwell time and return step length. By constructing the maximum entropy model, combined with the memory effect of interest, the reasons for the long-tailed distribution are explained respectively. Based on the analysis results, we propose an interest stickiness measurement algorithm for efficiently evaluating the attractiveness of web interest points to users. The regularity and predictability of group users' browsing interest points play a significant role in science, engineering, business promotion, development of the economy of attention and other specific fields.