On cold start for associative tag recommendation

Eder F. Martins; Fabiano M. Belém; Jussara M. Almeida; Marcos A. Gonçalves

doi:10.1002/asi.23353

On cold start for associative tag recommendation

Journal of the Association for Information Science and Technology ◽

10.1002/asi.23353 ◽

2015 ◽

Vol 67 (1) ◽

pp. 83-105 ◽

Cited By ~ 11

Author(s):

Eder F. Martins ◽

Fabiano M. Belém ◽

Jussara M. Almeida ◽

Marcos A. Gonçalves

Keyword(s):

Cold Start ◽

Tag Recommendation

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Exploiting syntactic and neighbourhood attributes to address cold start in tag recommendation

Information Processing & Management ◽

10.1016/j.ipm.2018.12.009 ◽

2019 ◽

Vol 56 (3) ◽

pp. 771-790 ◽

Cited By ~ 6

Author(s):

Fabiano M. Belém ◽

André G. Heringer ◽

Jussara M. Almeida ◽

Marcos A. Gonçalves

Keyword(s):

Cold Start ◽

Tag Recommendation

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

Program electronic library and information systems ◽

10.1108/prog-01-2017-0002 ◽

2017 ◽

Vol 51 (3) ◽

pp. 218-234 ◽

Cited By ~ 4

Author(s):

Muhammad Ali Masood ◽

Rabeeh Ayaz Abbasi ◽

Onaiza Maqbool ◽

Mubashar Mushtaq ◽

Naif R. Aljohani ◽

...

Keyword(s):

Latent Dirichlet Allocation ◽

Feature Space ◽

Cold Start ◽

Tag Recommendation ◽

Social Bookmarking ◽

Content Type ◽

Feature Spaces ◽

Multiple Feature ◽

Cold Start Problem ◽

Dirichlet Allocation

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.

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