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MFS-LDA: a multi-feature space tag recommendation model for cold start problem

Muhammad Ali Masood (Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)
Rabeeh Ayaz Abbasi (Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia) (Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)
Onaiza Maqbool (Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)
Mubashar Mushtaq (Department of Computer Science and Software Engineering, Forman Christian College, Lahore, Pakistan) (Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan)
Naif R. Aljohani (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)
Ali Daud (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia) (Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan)
Muhammad Ahtisham Aslam (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)
Jalal S. Alowibdi (Faculty of Computing and Information Technology, University of Jeddah, Jeddah, Saudi Arabia)

Program: electronic library and information systems

ISSN: 0033-0337

Article publication date: 5 September 2017

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Abstract

Purpose

Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem.

Design/methodology/approach

MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem).

Findings

Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods.

Originality/value

The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.

Keywords

Citation

Masood, M.A., Abbasi, R.A., Maqbool, O., Mushtaq, M., Aljohani, N.R., Daud, A., Aslam, M.A. and Alowibdi, J.S. (2017), "MFS-LDA: a multi-feature space tag recommendation model for cold start problem", Program: electronic library and information systems, Vol. 51 No. 3, pp. 218-234. https://doi.org/10.1108/PROG-01-2017-0002

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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