Abstract
Recommender systems have been proven useful in numerous contemporary applications and helping users effectively identify items of interest within massive and potentially overwhelming collections. Among the recommender system techniques, the collaborative filtering mechanism is the most successful; it leverages the similar tastes of similar users, which can serve as references for recommendation. However, a major weakness for the collaborative filtering mechanism is its performance in computing the pairwise similarity of users. Thus, the MapReduce framework was examined as a potential means to address this performance problem. This paper details the development and employment of the MapReduce framework, examining whether it improves the performance of a personal ontology based recommender system in a digital library. The results of this extensive performance study show that the proposed algorithm can scale recommender systems for all-pairs similarity searching.
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