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temporal-point-process
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ChOracle: A unified statistical framework for churn prediction
, Article IEEE Transactions on Knowledge and Data Engineering ; Volume 34, Issue 4 , 2022 , Pages 1656-1666 ; 10414347 (ISSN) ; Hosseini, S. A ; Pajouheshgar, E ; Mansouri, F ; Rabiee, H. R ; Sharif University of Technology
IEEE Computer Society
2022
Abstract
User churn is an important issue in online services that threatens the health and profitability of services. Most of the previous works on churn prediction convert the problem into a binary classification task where the users are labeled as churned and non-churned. More recently, some works have tried to convert the user churn prediction problem into the prediction of user return time. In this approach which is more realistic in real world online services, at each time-step the model predicts the user return time instead of predicting a churn label. However, the previous works in this category suffer from lack of generality and require high computational complexity. In this paper, we...
Improving The Performance of Recommendation Systems by Exploiting Relationships and Temporal Behavior of Users in Social Networks
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Online social services and stores has become more popular in recent years . With the growing number of products and services available in these online stores and websites , recommendation systems play an important role in predicting which products and services are good for each user so that they can buy the product they need or use the online services they want without wasting their time to find the right ones . We propose a method called Temporal Socio-Content Poisson Factorization (TSCPF) which aims to recommend the most probable document to be read by each user at any given time . We model users’ behaviors and interests by using temporal point processes to capture temporal dynamics of...
Analysis and Modeling of User Behavior over Social Media
, Ph.D. Dissertation Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
Nowadays many of us spend a big part of our daily times on social media.One of the most important research problems in social media analysis is how to engage users. The trace of user activity over these websites is a valuable resource for user understanding and engagement, but this data is very huge and unstructured. An approach to deal with this problem is user behavior modeling. In this process, first a behavioral model is considered for users, then using the activity data and the behavioral model, some parameters are learned. Finally, using the learned parameters, a user profile is constructed for each user. This profile can be used for user engagement and many other applications....
Continuous-time user modeling in presence of badges: a probabilistic approach
, Article ACM Transactions on Knowledge Discovery from Data ; Volume 12, Issue 3 , 2018 ; 15564681 (ISSN) ; Hosseini, A ; Tavakoli, E ; Rabiee, H. R ; Sharif University of Technology
Association for Computing Machinery
2018
Abstract
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 article,...
Continuous Time Modeling of Marked Events
, Ph.D. Dissertation Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
A great deal of information are continuously generated by users in different contexts such as social networks and online service providers in terms of temporal marked events. These events indicate that what happened to who by when and where.Modeling such events and predicting future ones has interesting applications in different domains such as item recommendation in online service providers and trending topic prediction in online social networks. However, complex longitudinal dependencies among such events makes the prediction task challenging. Moreover, nonstationarity of generative model of events and large size of events, makes the modeling and learning the models challenging.In this...
RedQueen: an online algorithm for smart broadcasting in social networks
, Article WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining, 2 February 2017 ; 2017 , Pages 51-60 ; 9781450346757 (ISBN) ; Upadhyay, U ; Rabiee, H. R ; Gomez Rodriguez, M ; Sharif University of Technology
Association for Computing Machinery, Inc
2017
Abstract
Users in social networks whose posts stay at the top of their followers' feeds the longest time are more likely to be noticed. Can we design an online algorithm to help them decide when to post to stay at the top? In this paper, we address this question as a novel optimal control problem for jump stochastic differential equations. For a wide variety of feed dynamics, we show that the optimal broadcasting intensity for any user is surprisingly simple - it is given by the position of her most recent post on each of her follower's feeds. As a consequence, we are able to develop a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post....
Steering social activity: a stochastic optimal control point of view
, Article Journal of Machine Learning Research ; Volume 18 , 2018 , Pages 1-35 ; 15324435 (ISSN) ; De, A ; Upadhyay, U ; Rabiee, H.R ; Gomez Rodriguez, M ; Sharif University of Technology
Microtome Publishing
2018
Abstract
User engagement in online social networking depends critically on the level of social activity in the corresponding platform-the number of online actions, such as posts, shares or replies, taken by their users. Can we design data-driven algorithms to increase social activity? At a user level, such algorithms may increase activity by helping users decide when to take an action to be more likely to be noticed by their peers. At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users. In this paper, we model social activity using the framework of marked temporal point processes,...