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Published in:
2013 | OriginalPaper | Chapter
Scaling Out Recommender System for Digital Libraries with MapReduce
Authors : Lun-Chi Chen, Ping-Jen Kuo, I-En Liao, Jyun-Yao Huang
Published in: Grid and Pervasive Computing
Publisher: Springer Berlin Heidelberg
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Recommender system can help users to effectively identify interested items from a potentially overwhelming huge collection of items, and it has been shown to be very useful in many e-commerce applications. Collaborative filtering (CF), which assumes that similar users may have similar tastes, is one of the most widely used Recommender system techniques. However, one of the major weaknesses for the CF mechanism is the computational cost in computing pairwise similarity of users. This paper attempts to tackle the computational problem of all pairs similarity using the MapReduce technique in the Hadoop framework. We give an overview of our development on using a parallel filtering algorithm to improve the performance of a personal ontology based recommender system for digital library. The experimental results show that the proposed algorithm can indeed scale out the recommender systems for all pairs search.