Continuous-Time User Modeling in Presence of Badges

Khodadadi, Ali; Hosseini, Seyed Abbas; Tavakoli, Erfan; Rabiee, Hamid R.

doi:10.1145/3162050

Cited by 7 publications

(6 citation statements)

References 55 publications

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“…In this work, we classify user behavioral models into two kinds according to the granularity of behavioral agents, i.e., the population-level model [13] and individual-level model [6]. Accordingly, we establish the population-level model and individual-level model based the customized derivative network, respectively:…”

Section: Model Enhancementmentioning

confidence: 99%

“…The time of day can be divided into four time intervals. We set four intervals of the hour: [0, 3), [6,11), [15,24), and [3, 6) ∪ [11,15), according to the time distribution in transactions. We further divide the attribute time into 8 unique values by distinguishing whether it is a weekday.…”

Section: Datasetsmentioning

confidence: 99%

“…For model classifications, one type is based on the behavioral agent since it is a critical factor of behavioral models. According to the granularity of agents, behavioral models can be further divided into the individual-level models [6], [7], [8], [9] and population-level models [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

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Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services

Wang

Zhu

2022

IEEE Trans. Dependable and Secure Comput.

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The vigorous development of e-commerce breeds cybercrime. Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce. Behavior-based methods are recognized as a promising method for online payment fraud detection. However, it is a big challenge to build high-resolution behavioral models by using low-quality behavioral data. In this work, we mainly address this problem from data enhancement for behavioral modeling. We extract fine-grained co-occurrence relationships of transactional attributes by using a knowledge graph. Furthermore, we adopt the heterogeneous network embedding to learn and improve representing comprehensive relationships. Particularly, we explore customized network embedding schemes for different types of behavioral models, such as the population-level models, individual-level models, and generalized-agentbased models. The performance gain of our method is validated by the experiments over the real dataset from a commercial bank. It can help representative behavioral models improve significantly the performance of online banking payment fraud detection. To the best of our knowledge, this is the first work to realize data enhancement for diversified behavior models by implementing network embedding algorithms on attribute-level co-occurrence relationships.

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Section: Model Enhancementmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services

Wang

Zhu

2022

IEEE Trans. Dependable and Secure Comput.

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“…Therefore, many research efforts have been directed toward predicting user churn, in recent years. Once the potential churners identified, the customer relationship management (CRM) systems can target them with appropriate incentives such as tailored promotions [4] or gamification methods [5] to sustain their interest in their current services.…”

Section: Introductionmentioning

confidence: 99%

ChOracle: A Unified Statistical Framework for Churn Prediction

Khodadadi¹,

Hosseini²,

Pajouheshgar³

et al. 2019

Preprint

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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 introduce ChOracle, an oracle that predicts the user churn by modeling the user return times to service by utilizing a combination of Temporal Point Processes and Recurrent Neural Networks. Moreover, we incorporate latent variables into the proposed recurrent neural network to model the latent user loyalty to the system. We also develop an efficient approximate variational algorithm for learning parameters of the proposed RNN by using back propagation through time. Finally, we demonstrate the superior performance of ChOracle on a wide variety of real world datasets.

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“…More recently, point process methods have a racted a lot of interest in user behavior modeling [12] and recommendation systems [5,6,10,19]. e paper [18] presents a rst Hawkes process approach to model the adoption of products, however, their model does not capture dynamic preferences and su er from inconsistent and negative rate functions.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Poisson Factorization for Temporal Recommendation

Hosseini

Alizadeh

Khodadadi

et al. 2017

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. ere are many variants of Poisson factorization methods who show state-of-theart performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time.In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback.RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item speci cation (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-ofthe-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.

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Continuous-Time User Modeling in Presence of Badges

Cited by 7 publications

References 55 publications

Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services

Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services

ChOracle: A Unified Statistical Framework for Churn Prediction

Recurrent Poisson Factorization for Temporal Recommendation

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