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Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms

Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms

Fahd Kalloubi, El Habib Nfaoui, Omar El Beqqali
Copyright: © 2017 |Volume: 13 |Issue: 1 |Pages: 19
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781522511571|DOI: 10.4018/IJSWIS.2017010105
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MLA

Kalloubi, Fahd, et al. "Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms." IJSWIS vol.13, no.1 2017: pp.63-81. http://doi.org/10.4018/IJSWIS.2017010105

APA

Kalloubi, F., Nfaoui, E. H., & El Beqqali, O. (2017). Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms. International Journal on Semantic Web and Information Systems (IJSWIS), 13(1), 63-81. http://doi.org/10.4018/IJSWIS.2017010105

Chicago

Kalloubi, Fahd, El Habib Nfaoui, and Omar El Beqqali. "Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms," International Journal on Semantic Web and Information Systems (IJSWIS) 13, no.1: 63-81. http://doi.org/10.4018/IJSWIS.2017010105

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Abstract

Twitter is one of the most popular microblog service providers, in this microblogging platform users use hashtags to categorize their tweets and to join communities around particular topics. However, the percentage of messages incorporating hashtags is small and the hashtags usage is very heterogeneous as users may spend a lot of time searching the appropriate hashtags for their messages. In this paper, the authors present an approach for hashtag recommendations in microblogging platforms by leveraging semantic features. Moreover, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Also, users are interested by fresh and specific hashtags due to the rapid growth of microblogs, thus, the authors propose a time popularity ranking strategy. Furthermore, they study the combination of these ranking strategies. The experiment results conducted on a large dataset; show that their approach improves respectively lexical and semantic based recommendation by more than 11% and 7% on recommending 5 hashtags.

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