Bookmarks Recommendation in Bibsonomy using Community Detection

Mahmoud Saoud () and Zakaria Saoud ()
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Mahmoud Saoud: Ecole Normale Supérieure
Zakaria Saoud: University of Sciences and Technologies Houari Boumediene

The Review of Socionetwork Strategies, 2018, vol. 12, issue 2, 253-260

Abstract: Abstract Several academic social networks have emerged to help researchers who need to search for documents relevant to their interests. The recommendation has been adopted in many websites to suggest relevant documents to users according to their profiles. However, many academic social networks and digital libraries still lack recommendations. In this paper, we propose a new document recommendation approach for the academic social bookmarking website: Bibsonomy. In our method, we use a community detection technique to identify related users. Then, for each target user, the recommended documents are selected from their learning communities. Experimental results show that the proposed method performs better than state-of-the-art recommendation methods.

Keywords: Recommendation systems; Bibsonomy; Community detection; Data clustering (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12626-018-0024-7

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