HNP3: A Hierarchical Nonparametric Point Process for Modeling Content Diffusion over Social Media

Hosseini, Seyed Abbas; Khodadadi, Ali; Arabzadeh, Ali; Rabiee, Hamid R.

doi:10.1109/icdm.2016.0117

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…We go beyond their study by deepening our understanding of the (noisy) properties of the Hawkes loglikelihood as a function of the decay. Methodologically, our Bayesian approach relates to Hosseini et al's [41]. Those authors infer the decay parameter by assuming a Gamma prior and computing the mean of samples from the posterior (as part of a larger inference problem).…”

Section: Discussionmentioning

confidence: 99%

Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels

Santos,

Lemmerich,

Helic

2021

Preprint

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As a tool for capturing irregular temporal dependencies (rather than resorting to binning temporal observations to construct time series), Hawkes processes with exponential decay have seen widespread adoption across many application domains, such as predicting the occurrence time of the next earthquake or stock market spike. However, practical applications of Hawkes processes face a noteworthy challenge: There is substantial and often unquantified variance in decay parameter estimations, especially in the case of a small number of observations or when the dynamics behind the observed data suddenly change. We empirically study the cause of these practical challenges and we develop an approach to surface and thereby mitigate them. In particular, our inspections of the Hawkes process likelihood function uncover the properties of the uncertainty when fitting the decay parameter. We thus propose to explicitly capture this uncertainty within a Bayesian framework. With a series of experiments with synthetic and real-world data from domains such as "classical" earthquake modeling or the manifestation of collective emotions on Twitter, we demonstrate that our proposed approach helps to quantify uncertainty and thereby to understand and fit Hawkes processes in practice.

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Section: Discussionmentioning

confidence: 99%

Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels

Santos,

Lemmerich,

Helic

2021

Preprint

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“…However, when modeling one sequence per user-item pair, conventional Hawkes processes model each item's sequences individually and do not rely on the similarities between different items. Thus, they cannot infer parameters for items that have unseen data (Hosseini et al 2016;Choi et al 2015;Mei and Eisner 2017;Du et al 2016a). In some recent work, low-rank personalized Hawkes models aim to address this problem (Du et al 2015b), usually with the help of auxiliary features to reinforce the lowrank assumption Sun 2018, 2019).…”

Section: Introductionmentioning

confidence: 99%

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Yao,

Zhao,

Sahebi

et al. 2021

Preprint

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Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis. Traditionally, these models parameterize each process independently and assume that the history of each point process can be fully observed. Such models could however be inefficient or even prohibited in certain real-world applications, such as in the field of education, where such assumptions are violated. Motivated by the problem of detecting and predicting student procrastination in students Massive Open Online Courses (MOOCs) with missing and partially observed data, in this work, we propose a novel personalized Hawkes process model (RCHawkes-Gamma) that discovers meaningful student behavior clusters by jointly learning all partially observed processes simultaneously, without relying on auxiliary features. Our experiments on both synthetic and real-world education datasets show that RCHawkes-Gamma can effectively recover student clusters and their temporal procrastination dynamics, resulting in better predictive performance of future student activities. Our further analyses of the learned parameters and their association with student delays show that the discovered student clusters unveil meaningful representations of various procrastination behaviors in students.

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“…Second, these models need to choose the length of time-steps to discretize the time, which is a challenging task. Actually, the precise time interval or the exact distance between two actions carries a great deal of information about the dynamics of the underlying systems and hence the emerging continuous-time models that consider the exact timing of events are more applicable to real world scenarios [53,58,56,51,33,22,23,31].…”

Section: Introductionmentioning

confidence: 99%

“…Continues-Time modeling of user activities over social media has gained considerable attention in recent years. Some preliminary works tried to model the user behavior and predict the future user actions using the time of users' actions and the impact of activity of friends on each other [53,58].More recently, some works tried to jointly model the content and time of actions [33,22,23]. The main drawback of the aforementioned methods is that they only consider the impact of friends actions on user activity and ignore other factors such as gamification elements, while considering the ways in which the gamification elements impact user activities and participation in the site, is very important in user modeling.…”

Section: Introductionmentioning

confidence: 99%

Continuous-Time User Modeling in Presence of Badges

Khodadadi

Hosseini

Tavakoli

et al. 2018

ACM Trans. Knowl. Discov. Data

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User modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements, are among those factors that are neglected, while they have a strong impact on user participation in online services. In this paper, we propose interdependent multi-dimensional temporal point processes that capture the impact of badges on user participation besides the peer influence and content factors. We extend the proposed processes to model user actions over the community

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HNP3: A Hierarchical Nonparametric Point Process for Modeling Content Diffusion over Social Media

Cited by 10 publications

References 8 publications

Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels

Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels

Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Continuous-Time User Modeling in Presence of Badges

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