Multi-dimensional attributes and measures for dynamical user profiling in social networking environments

Zhou, Xiaokang; Wang, Wei; Jin, Qun

doi:10.1007/s11042-014-2230-9

Cited by 37 publications

(2 citation statements)

References 29 publications

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“…The first paper entitled "Multi-Dimensional Attributes and Measures for Dynamical User Profiling in Social Networking Environments" by Xiaokang Zhou et al [12] addresses a promising issue in regard to the individual information seeking process in an open social network. A universal user model, namely, Dynamically Socialized User Networking, is proposed to well describe multidimensional and correlated user characteristics and properties.…”

Section: Summary Of Accepted Papersmentioning

confidence: 99%

Social Media Services and Technologies Towards Web 3.0

Yen

Zhang

Waluyo

et al. 2015

Multimed Tools Appl

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Section: Summary Of Accepted Papersmentioning

confidence: 99%

Social Media Services and Technologies Towards Web 3.0

Yen

Zhang

Waluyo

et al. 2015

Multimed Tools Appl

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“…The static features include age, education which cannot change over time and the dynamic features include location, interest which can vary according to time. The user profiling method is an effective way of providing such information services [6]. WPS includes demographic data, user's interest, preferences and their previous history, which require and maintain information about customers.…”

Section: Introductionmentioning

confidence: 99%

User profiling for web personalization using multi agent and DBSCAN based approach

Sahoo

Ratha

2018

IJET

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The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic User Profiles have been maintained by introducing a method called Density-Based Spa-tial Clustering of Applications with Noise-User Profiling (DBSCAN-UP). The mapping web pages, construct the ontological concepts, which represent the UI, and the interests of users are learned by the reference ontology, which are used to map the visited web pages. The process of storage, management and adaptation of UI is facilitated by multi-agent system. The different user browsing behaviors learning and adapting capability is built in the proposed system and the efficiency of the DBSCAN-UP model is evaluated by the series of experi-ments. The accuracy of the DBSCAN-UP was achieved up to 5% compared to the existing methods.

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Rio Olympics 2016 on Twitter: A Descriptive Analysis

Sharma

Gupta

2020

Computational Methods and Data Engineering

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Multi-dimensional attributes and measures for dynamical user profiling in social networking environments

Cited by 37 publications

References 29 publications

Social Media Services and Technologies Towards Web 3.0

Social Media Services and Technologies Towards Web 3.0

User profiling for web personalization using multi agent and DBSCAN based approach

Rio Olympics 2016 on Twitter: A Descriptive Analysis

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