Predicting user activity level in social networks

Zhu, Yin; Zhong, Erheng; Pan, Sinno Jialin; Wang, Xiao; Zhou, Min; Yang, Qiang

doi:10.1145/2505515.2505518

Cited by 45 publications

(22 citation statements)

References 27 publications

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“…From the perspective of user lives, studies such as Kairam S R et al [13] studied the development of user's life in social network. Zhu Y et al [14] learned the social network user level of activity. Danescu et al [15] paid attention to the user's life cycle from the perspective of community.…”

Section: Related Workmentioning

confidence: 99%

“…Zhu Y et al [14]studied user activity levels in social network, Long X et al [17] researched user relationship in social network.User churn model: there are many domestic and foreign scholars using data mining method to build user churn model, including classification model, the regression model, the clustering model, time series analysis. Studies such as Dror G et al [18] tried a variety of models in the churn prediction, including Logistic regression, SVM, KNN.…”

Section: Related Workmentioning

confidence: 99%

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A Churn Prediction Iterative Model in Social Network Based on Relationship Strength

Liu¹,

Liu²,

Xiao³

2017

dtcse

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Abstract. The existing research of churn prediction in social network mainly consider single individual behavior and adjacent relationship structure, we propose a churn prediction iterative model in social network based on relationship strength. First of all, according to the traditional model, we put forward a hierarchical structure approach to measure the strength of social relationship based on the traditional user relationships and the complexity of social theory. Secondly, from the two angles of the user's activity and user's influence, we apply iterative way to calculate the influence factor of the social environment. And based on conditional probability model, we put forward a churn prediction iterative model in social network (CIM). Experiments show that the model is suitable for the churn prediction in social network with a good effect, and improve the accuracy on churn prediction in social network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Churn Prediction Iterative Model in Social Network Based on Relationship Strength

Liu¹,

Liu²,

Xiao³

2017

dtcse

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“…This behaviour includes but is not limited to predicting one's location at a particular time [22], customer preferences [26], user activities at home [20,9], and behaviour on social media [27,6]. As a result, a variety of statistical models have been used for predicting user behaviour.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, analysis and evaluation of the time needed for training and predicting user behaviour is often omitted [9,21]. Predicting user behaviour on social media has included, for example, predicting the posting time of messages in Q&A forums [25,12], churn of users [27], response time to a tweet [24]. For example, the authors in [27] focused on churn prediction in social networks where the authors proposed using a modified Logistic Regression (LR) model.…”

Section: Related Workmentioning

confidence: 99%

An Investigation on Online Versus Batch Learning in Predicting User Behaviour

Burlutskiy

Petridis

Fish

et al. 2016

Research and Development in Intelligent Systems XXXIII

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An investigation on how to produce a fast and accurate prediction of user behaviour on the Web is conducted. First, the problem of predicting user behaviour as a classification task is formulated and then the main problems of such real-time predictions are specified: the accuracy and time complexity of the prediction. Second, a method for comparison of online and batch (offline) algorithms used for user behaviour prediction is proposed. Last, the performance of these algorithms using the data from a popular question and answer platform, Stack Overflow, is empirically explored. It is demonstrated that a simple online learning algorithm outperforms state-of-the-art batch algorithms and performs as well as a deep learning algorithm, Deep Belief Networks. The proposed method for comparison of online and offline algorithms as well as the provided experimental evidence can be used for choosing a machine learning set-up for predicting user behaviour on the Web in scenarios where the accuracy and the time performance are of main concern.

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“…User/product engagement analysis is one of the most valuable fields of research for companies that needs to promote their services on targeted audiences: in recent years, many works addressed the issue of predicting users' future activities based on their past social behavior, thanks to the fertile ground provided by social media like Facebook and Twitter. For example, Zhu et al (2013) conduct experiments on the social media Renren using a social customer relationship management (Social CRM) model, obtaining superior performance when compared with traditional supervised learning methods. Other works focus in particular on the prediction of churn, i.e., the loss of customers.…”

Section: Activity Prediction and Social Targetingmentioning

confidence: 99%

Homophilic network decomposition: a community-centric analysis of online social services

Rossetti

Pappalardo

Kikas

et al. 2016

Soc. Netw. Anal. Min.

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In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks-Skype, LastFM and Google?-and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google?.

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Predicting user activity level in social networks

Cited by 45 publications

References 27 publications

A Churn Prediction Iterative Model in Social Network Based on Relationship Strength

A Churn Prediction Iterative Model in Social Network Based on Relationship Strength

An Investigation on Online Versus Batch Learning in Predicting User Behaviour

Homophilic network decomposition: a community-centric analysis of online social services

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