Mining User Development Signals for Online Community Churner Detection

Rowe, Matthew

doi:10.1145/2798730

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…In this study, nodes are memers of the online community instead of web pages, and and links are based on the responses sent between them. Then according to the questions and links among people , the Chief Executive Network (CEN) is drawn [36]. At CEN, each user is linked to those who answered his/her questions, and each link is given a certain weight depending on the number of answers and the field of expertise.…”

Section: Phase Iii: Calculation Of the Attractivenessmentioning

confidence: 99%

Context-Based Expert Finding in Online Communities Using Ant Colony Algorithm

Sharifian¹,

Abdolvand²,

Harandi³

2020

jist

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Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user's questions with the intended domain. The proposed algorithm determines the level of people's expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community.

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Section: Phase Iii: Calculation Of the Attractivenessmentioning

confidence: 99%

Context-Based Expert Finding in Online Communities Using Ant Colony Algorithm

Sharifian¹,

Abdolvand²,

Harandi³

2020

jist

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“…For creating training data, we consider the dealers who have not done any sales transactions from the last 120 days as churned dealers. The logic is also described in [11]. This number of days can be customized according to different use cases and particularly depends upon the average sales gap of the customer in that business.…”

Section: Feature Engineeringmentioning

confidence: 99%

Prediction and Causality analysis of churn using deep learning

Shah¹,

Adiga²,

Bhat³

et al. 2019

6th International Conference on Computer Science, Engineering and Information Technology (CSEIT-2019)

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In almost every type of business a retention stage is very important in the customer life cycle because according to market theory, it is always expensive to attract new customers than retaining existing ones. Thus, a churn prediction system that can predict accurately ahead of time, whether a customer will churn in the foreseeable future and also help the enterprises with the possible reasons which may cause a customer to churn is an extremely powerful tool for any marketing team. In this paper, we propose an approach to predict customer churn for nonsubscription based business settings. We suggest a set of generic features that can be extracted from sales and payment data of almost all non-subscription based businesses and can be used in predicting customer churn. We have used the neural network-based Multilayer perceptron for prediction purposes. The proposed method achieves an F1-Score of 80% and a recall of 85%, comparable to the accuracy of churn prediction for subscription-based business settings. We also propose a system for causality analysis of churn, which will predict a set of causes which may have led to the customer churn and helps to derive customer retention strategies.

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“…Based on the players' playtime distributions introduced in Section III and motivated by [15], we use the notion of entropy from information theory as the metric to characterize variance and change in playtime [16]. Given a probability distribution p(•), its entropy is defined as…”

Section: A Entropy and Playtime Patternmentioning

confidence: 99%

Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity

Yang

Huang²,

Zeng³

et al. 2019

Preprint

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In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime. In particular, we measure playtime regularity using the notion of entropy and cross-entropy from information theory. After we calculate the playtime regularity of players from data sets of six free online games of different types. We leverage information from players' playtime regularity in the form of universal features for churn prediction. Experiments show that our developed features are better at predicting churners compared to baseline features. Thus, the experiment results imply that our proposed features could utilize the information extracted from players' playtime more effectively than related baseline playtime features.

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Mining User Development Signals for Online Community Churner Detection

Cited by 8 publications

References 26 publications

Context-Based Expert Finding in Online Communities Using Ant Colony Algorithm

Context-Based Expert Finding in Online Communities Using Ant Colony Algorithm

Prediction and Causality analysis of churn using deep learning

Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity

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