Social network survey paper

Ehrlich, Donna M.

doi:10.1504/ijlic.2006.010330

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…Thus, a SN structure is constituted by a set of 'nodes' connected with different 'ties' [12]. The cardinality of nodes expands dynamically, especially on the web, as new nodes and profiles are created continuously, while further populating the social web [13]. Hence, network thinking has contributed a number of important insights about social power while the different network analysis approach emphasize that power is inherently relational.…”

Section: Related Work In Sna and Churn Prediction Strategies In Crmmentioning

confidence: 99%

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Ibitoye¹,

Onifade²

2022

Journal on Big Data

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Community based churn prediction, or the assignment of recognising the influence of a customer's community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer's opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern era, customers residing online predominantly and collectively decide and determines the momentum of churn prediction, retention and decision support. In existing SNA approaches, customers are classified as churner or non-churner (1 or 0). Oftentimes, the customer's opinion is also neglected and the network structure of community members are not exploited. Consequently, the pattern and influential abilities of customers' opinion on relative members of the community are not analysed. Thus, the research developed a Churn Service Information Graph (CSIG) to define a quadruple churn category (churner, potential churner, inertia customer, premium customer) for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA. The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group. Consequently, every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition, retention and/or decision supports in churn management.

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Section: Related Work In Sna and Churn Prediction Strategies In Crmmentioning

confidence: 99%

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Ibitoye¹,

Onifade²

2022

Journal on Big Data

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“…Milan Bjelica determined the most important factors of a TV contents recommender and proposed an analyzing model for the estimation of user interest based on a contentbased approach [8]. SeungGwan Lee et al suggested a personalized TV contents recommender system for the cloud computing environment [9].…”

Section: Tv Contents Recommender Systemsmentioning

confidence: 99%

Personalized TV Contents Recommender System Using Collaborative Context tagging-based User’s Preference Prediction Technique

Lee

Kwon

2014

IJMUE

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“…Among previous TV contents recommender systems, the TV advisor developed by Das and Horst makes use of explicit techniques to generate recommendations for a TV viewer [4]. Milan Bjelica determined the most important factors of a TV contents recommender and proposed an analyzing model for the estimation of user interest based on a content-based approach [5]. SeungGwan Lee et al suggested a personalized TV contents recommender system for the cloud computing environment [6].…”

Section: Related Workmentioning

confidence: 99%

A Smart TV Application for Personalized Recommender Services

Lee¹,

Kwon

2013

Interdisciplinary Research Theory and Technology

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With the TV channel explosion, many people face an exhausting task of having to find something to watch on TV that fits their interest. Despite the excessive increase of TV contents, there are few considerations in the development of novel methods for personalized smart TV content recommender services. In this paper, we define an application architecture of personalized TV content recommender service. In addition, for the personalized recommender service, we consider the use of user's context data increasing the user's satisfaction about the recommender service.

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Social network survey paper

Cited by 10 publications

References 11 publications

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Personalized TV Contents Recommender System Using Collaborative Context tagging-based User’s Preference Prediction Technique

A Smart TV Application for Personalized Recommender Services

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