Using Strong, Acquaintance and Weak Tie Strengths for Modeling Relationships in Facebook Network

Kumar, Arnab; Rao, T. Venkat Narayana; Nagpal, Sushama

doi:10.1007/978-3-642-32129-0_23

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Gilbert et al [19] modeled link strengths between Facebook users, as a linear combination of the predictive variables, plus the terms for interactions and network structure. Arnab et al [20] utilized an unsupervised divisive hierarchical clustering algorithm to classify link strengths into four categories, namely strong, weak, strong acquaintance and weak acquaintance. Unlike [18,19,20] which only performed the binary task to predict whether link strengths are strong or not, Xiang et al [21] gave a full spectrum of relationship strengths from weak to strong.…”

Section: A Link Strengthmentioning

confidence: 99%

“…Arnab et al [20] utilized an unsupervised divisive hierarchical clustering algorithm to classify link strengths into four categories, namely strong, weak, strong acquaintance and weak acquaintance. Unlike [18,19,20] which only performed the binary task to predict whether link strengths are strong or not, Xiang et al [21] gave a full spectrum of relationship strengths from weak to strong. Xiang et al [21] assumed that relationship strength was the hidden effect of profile similarity and hidden cause of user interaction.…”

Section: A Link Strengthmentioning

confidence: 99%

“…But in fact user interactions also contribute to relationship strength, therefore our model takes both profile similarity and user interaction as the joint cause of relationship strength. Neither these models [18,19,20,21] considered the temporal aspect of link strengths, nor they analyzed the evolution of relationship strengths. However in the real world, relationship strengths may fluctuate over time when some interactions happen or some profile attributes change.…”

Section: A Link Strengthmentioning

confidence: 99%

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Incorporating trust relationships in collaborative filtering recommender system

Shen

Long

2015

2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distribu

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Nowadays with the readily accessibility of online social networks (OSNs), people are facilitated to share interesting information with friends through OSNs. Undoubtedly these sharing activities make our life more fantastic. However, meanwhile one challenge we have to face is information overload that we do not have enough time to review all of the content broadcasted through OSNs. So we need to have a mechanism to help users recognize interesting items from a large pool of content. In this project, we aim at filtering unwanted content based on the strength of trust relationships between users. We have proposed two kinds of trust models-basic trust model and source-level trust model. The trust values are estimated based on historical user interactions and profile similarity. We estimate dynamic trusts and analyze the evolution of trust relationships over dates. We also incorporate the auxiliary causes of interactions to moderate the noisy effect of user's intrinsic tendency to perform a certain type of interaction. In addition, since the trustworthiness of diverse information sources are rather distinct, we further estimate trust values at source-level. Our recommender systems utilize several types of Collaborative Filtering (CF) approaches, including conventional CF (namely user-based, item-based, singular value decomposition (SVD)based), and also trust-combined user-based CF. We evaluate our trust models and recommender systems on Friendfeed datasets. By comparing the evaluation results, we found that the recommendations based on estimated trust relationships were better than conventional CF recommendations.

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Section: A Link Strengthmentioning

confidence: 99%

Section: A Link Strengthmentioning

confidence: 99%

Section: A Link Strengthmentioning

confidence: 99%

See 1 more Smart Citation

Incorporating trust relationships in collaborative filtering recommender system

Shen

Long

2015

2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distribu

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Using Machine Learning to Compare the Information Needs and Interactions of Facebook: Taking Six Retail Brands as an Example

Chen

2021

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This study explores the interactive characteristics of the public, referencing existing data mining methods. This research attempts to develop a community data mining and integration technology to investigate the trends of global retail chain brands. Using social media mining and ensemble learning, it examines key image cues to highlight the various reasons motivating participation by fans. Further, it expands the discussion on image and marketing cues to explore how various social brands induce public participation and the evaluation of information efficiency. This study integrates random decision forests, extreme gradient boost, and adaboost for statistical verification. From 1 January 2011 to 31 December 2019, the studied brands published a total of 25,538 posts. The study combines community information and participation in its research framework. The samples are divided into three categories: retail food brand, retail home improvement brand, and retail warehouse club brand. This research draws on brand image and information cue theory to design the theoretical framework, and then uses behavior response factors for the theoretical integration. This study contributes a model that classifies brand community posts and mines related data to analyze public needs and preferences. More specifically, it proposes a framework with supervised and ensemble learning to classify information users′ behavioral characteristics.

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Using Strong, Acquaintance and Weak Tie Strengths for Modeling Relationships in Facebook Network

Cited by 2 publications

References 19 publications

Incorporating trust relationships in collaborative filtering recommender system

Incorporating trust relationships in collaborative filtering recommender system

Using Machine Learning to Compare the Information Needs and Interactions of Facebook: Taking Six Retail Brands as an Example

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