Evolution properties of online user preference diversity

Abstract

h i g h l i g h t s• The evolution pattern of the online user preference is investigated.• The user rating preference would become diverse and then get centralized finally.• The correlation between the user ratings and the object qualities keeps increasing.Detecting the evolution properties of online user preference diversity is of significance for deeply understanding online collective behaviors. In this paper, we empirically explore the evolution patterns of online user rating preference, where the preference diversity is measured by the variation coefficient of the user rating sequence. The statistical results for four real systems show that, for movies and reviews, the user rating preference would become diverse and then get centralized finally. By introducing the empirical variation coefficient, we present a Markov model, which could regenerate the evolution properties of two online systems regarding to the stable variation coefficients. In addition, we investigate the evolution of the correlation between the user ratings and the object qualities, and find that the correlation would keep increasing as the user degree increases. This work could be helpful for understanding the anchoring bias and memory effects of the online user collective behaviors.

Structure-oriented prediction in complex networks

“…Li et al [224] built a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013 and analyzed the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. There are some works focus on the evolution patterns of user-object bipartite networks in a large time span [225,226].…”

Coupling diversity across human behavior spaces

“…Specifically, we randomly assign an individual's total check-ins to his/her visited locations, ensuring that each location has been visited at least once and the total number of visits remains unchanged. For example, the visiting pattern of an individual with 100 check-ins at 5 locations, say [20,30,15,15,20], may be shuffled to [37,31,14,5,13] or The correlation between the number of locations l and spatial diversity SD. The y-axis represents the ratio of diversity value between real and null trajectories.…”

“…which sums over all unique values of degree k j among the individual's friends N (i), and p(k) is the non-zero fraction of the individual's friends with degree k. Considering the large space of possible values k j could take (usually varying from several to hundreds), it is less likely that two friends collide with the same degree, and, therefore, any possible value of friend degree gets an even probability, making balance diversity a trivial inverse of variety diversity. For example, say both users A and B have 5 friends, whose degrees are [6,7,8,9,10] and [1,10,20,30,40], respectively. Equation (1) gives H A = H B = 2.322, while user A is believed intuitively to be less diverse because his friends have similar degrees.…”

Social signature identification of dynamical social networks

“…Since the scale of meaningful relations is limited in OSNs, recognizing the closest friends and meaningful interactions are crucial tasks for spreading social influence [20][21][22], discovering online user's behavioral preference [23][24][25], and providing better online service in recommendation systems [26,27]. Bond et al [21] investigated the Facebook network with 61 million users for political mobilization and found that close friends exerted more influence on voters mobilized than the message itself.…”