Collective attention and the dynamics of group deals
Mao Ye,Thomas Sandholm,Chunyan Wang,Christina Aperjis,Bernardo A. Huberman +4 more
- pp 1205-1212
TLDR
In this paper, the authors studied the group purchasing behavior of daily deals in Groupon and LivingSocial and formulated a predictive dynamic model of collective attention for group buying behavior using large data sets from both groups.Abstract:
We present a study of the group purchasing behavior of daily deals in Groupon and LivingSocial and formulate a predictive dynamic model of collective attention for group buying behavior. Using large data sets from both Groupon and LivingSocial we show how the model is able to predict the success of group deals as a function of time.We find that Groupon deals are easier to predict accurately earlier in the deal lifecycle than LivingSocial deals due to the total number of deal purchases saturating quicker. One possible explanation for this is that the incentive to socially propagate a deal is based on an individual threshold in LivingSocial, whereas in Groupon it is based on a collective threshold which is reached very early. Furthermore, the personal benefit of propagating a deal is greater in LivingSocial.read more
Citations
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Journal ArticleDOI
Emergence of scaling in human-interest dynamics
Zhi-Dan Zhao,Zimo Yang,Zi-Ke Zhang,Zi-Ke Zhang,Tao Zhou,Zi-Gang Huang,Zi-Gang Huang,Ying-Cheng Lai +7 more
TL;DR: In this paper, the authors investigated the scaling behaviors associated with human-interest dynamics, including the length of continuous interest, the return time of visiting certain interest and the interest ranking and transition.
Proceedings ArticleDOI
Daily deals: prediction, social diffusion, and reputational ramifications
TL;DR: A study of the economics of daily deals on the web, based on a dataset compiled by monitoring Groupon and LivingSocial sales in 20 large cities over several months, provides evidence that daily deal sites benefit from significant word-of-mouth effects during sales events, consistent with results predicted by cascade models.
Journal ArticleDOI
Ceiling effect of online user interests for the movies
TL;DR: The information entropy is introduced to measure the diversity of the user interests and shows that as the user degree increases, the entropy increases from the lowest value at first to the highest value and then begins to fall, which indicates that the interests of the small-degree and large-degree users are more centralized, while the interests are more diverse.
Journal ArticleDOI
The Attention Automaton: Sensing Collective User Interests in Social Network Communities
TL;DR: The proposed Attention Automaton is proposed, a probabilistic finite automata that can estimate the collective attention of some user community and can predict audience reception of impending trends based on categorical filters and inherent oscillations in user activity.
Journal ArticleDOI
Memory effect of the online rating for movies
TL;DR: This paper investigates the correlations between the users’ rating behaviors and the real-time updated average ratings of objects given from other users' previous ratings and shows that in general there is a linear correlation with slope one between them when the displayed average ratings are between 2.0 and 4.5.
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