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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Published on: February 23, 2019

Community-aware user profile enrichment in folksonomy.

Haoran Xie¹, Qing Li², Xudong Mao¹

¹Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region.

Neural Networks : the Official Journal of the International Neural Network Society

|June 9, 2014

Summary

This study introduces a novel method to enrich user profiles using latent communities in collaborative tagging systems. By identifying user communities, it overcomes data sparsity for better personalized applications.

Keywords:

Big data Community Folksonomy Social media User profiling

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Area of Science:

Information Science
Computer Science
Web Science

Background:

Collaborative tagging systems (folksonomies) are prevalent in Web 2.0, generating large datasets.
User profiling is crucial for personalized search and recommender systems.
Conventional methods struggle with data sparsity, limiting profile accuracy.

Purpose of the Study:

To propose a novel method for enriching user profiles by leveraging latent user communities within folksonomy data.
To address the data sparsity problem in user profiling.

Main Methods:

Extracting tag-based user profiles from a folksonomy tripartite graph.
Constructing a multi-faceted folksonomy graph integrating tag and image affinities.
Employing random walk distance to measure unified user similarities.
Utilizing a prototype-based clustering method to identify user communities for profile enrichment.

Main Results:

The proposed method effectively enriches user profiles by incorporating community information.
Experimental results on a public dataset demonstrate superior performance compared to existing approaches.
The approach successfully identifies latent user communities to enhance profile accuracy.

Conclusions:

The developed method offers a robust solution for user profile enrichment in folksonomy systems.
Leveraging latent user communities significantly improves the quality and utility of user profiles.
This research contributes to more effective personalized search and recommendation systems.