A parallel tag affinity computation for social tagging systems using MapReduce. (1st January 2014)

• Record Type: Journal Article
• Authors: Kim, Hyunwoo; Lee, Taewhi; Kim, Hyoung-Joo
• Is Part Of: International journal of big data intelligence. Volume 1:Number 3(2014)
• Page Start: 141
• Page End: 150
• Publication Date: 2014-01-01
• Subjects: parallelisation -- social tagging -- MapReduce -- Hadoop -- tag affinity -- tag cooccurrence frequency -- bigram -- big data; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

Abstract

Tag affinity is the relationship between tags. It is a useful information for search and recommendation in social tagging systems. Tag affinity is measured by several types of tag cooccurrence frequency. The computation of tag affinity is a time-consuming task as the tagging information is accumulated. To alleviate this problem, we propose a parallel tag affinity computation method using MapReduce. We present MapReduce algorithms for computing three types of tag affinity measures: macro, micro, and bigram tag cooccurrence frequency. Our experimental results show that the proposed MapReduce-based approach not only significantly outperforms existing methods based on a relational database but also provides high scalability. To the best of our knowledge, this approach is the first tag affinity computation on MapReduce.