Friend Recommendation Based on the Similarity of Micro-blog User Model

Tang, Fan; Zhang, Bofeng; Zheng, Jianxing; Gu, Yajun

doi:10.1109/greencom-ithings-cpscom.2013.415

Cited by 16 publications

(4 citation statements)

References 6 publications

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“…For example, the Bayesian Hierarchical Latent-Factor model (BHLFM) was designed to uncover hidden affiliations of members to multiple communities and roles [69]. Users who simultaneously affiliate with several communities in the analyzed network are considered to have an important role due to their structural location in the network that enables them to potentially control the spread of information among those communities [94]. These users who affiliate with several communities have served central roles in increasing the spread of information reach about presidential campaigns as well as propaganda [95].…”

Section: Social Rolesmentioning

confidence: 99%

Role-Aware Information Spread in Online Social Networks

Bartal

Jagodnik

2021

Entropy

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Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

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Section: Social Rolesmentioning

confidence: 99%

Role-Aware Information Spread in Online Social Networks

Bartal

Jagodnik

2021

Entropy

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“…15 In social networks, the measure of users' similarity is used to find a potential friend or interesting content. 16 Tang et al 17 employed similar micro-blogs as the basis for issuing a friend recommendation by measuring the interactions between users and topics that a user posted. However, because the independent item is evaluated using cosine similarity and Pearson correlation, this approach is not applicable for measuring the similarity of travel sequences.…”

Section: User Similaritymentioning

confidence: 99%

Personalized service recommendations for travel using trajectory pattern discovery

Duan

Tang

Gong

et al. 2018

International Journal of Distributed Sensor Networks

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Service recommendations help travelers locate en route traffic information service of interest in a timely manner. However, recommendations based on simple traffic information, such as the number of requests for the location of a facility, fail to consider an individual's preferences. Most existing work on improving service recommendations has continued to utilize the same ratings and rankings of services without consideration of diverse users' demands. The challenge remains to push forward the modeling of spatiotemporal trajectories to improve service recommendations. In this research, we proposed a new method to address the above challenge. We developed a personalized service-trajectory correlation that could recommend the most appropriate services to users. In addition, we proposed the use of ''congeniality'' probability to measure the service demand similarity of two travelers based on their service-visiting behaviors and preferences. We employed a clustering-based scheme, taking into account the spatiotemporal dimensions to refine the trajectories at each spot where travelers stayed at a certain point in time. Experiments were conducted employing a real global positioning system-based dataset. The test results demonstrated that our proposed approach could reduce the deviation of the trajectory measurement to 10% and enhance the success rates of the service recommendations to 60%.

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“…Most recommendation system tends to recommend users who have similar tastes to a specify user. Similarity is defined by attribute such as age, sex, city or interests in most cases [7]. Although this kind of recommendation systems is easy to use, its application is limited.…”

Section: Related Workmentioning

confidence: 99%

A Trust-based Friend Recommendation Strategy in Microblog

Cui¹,

Liu²

2015

Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics

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With the increasing popularity of online social networks (OSNs), it is becoming a challenge to recommend proper friends for a target user. Recommendation system based on trust is one of the approaches having been developed in recent years. In this paper, we introduce a novel personalized recommendation strategy which is based on trust relationship. The experimental results show the strategy can more effectively use implicit and explicit connections between the OSNs users.

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Friend Recommendation Based on the Similarity of Micro-blog User Model

Cited by 16 publications

References 6 publications

Role-Aware Information Spread in Online Social Networks

Role-Aware Information Spread in Online Social Networks

Personalized service recommendations for travel using trajectory pattern discovery

A Trust-based Friend Recommendation Strategy in Microblog

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