A parallel tag affinity computation for social tagging systems using MapReduce
by Hyunwoo Kim; Taewhi Lee; Hyoung-Joo Kim
International Journal of Big Data Intelligence (IJBDI), Vol. 1, No. 3, 2014

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.

Online publication date: Tue, 30-Dec-2014

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