Chapter 6. Distributing recommendation computations
This chapter covers
- Analyzing a massive data set from Wikipedia
- Producing recommendations with Hadoop and distributed algorithms
- Pseudo-distributing existing nondistributed recommenders
This book has looked at increasingly large data sets: from 10s of preferences, to 100,000, to 10 million, and then 17 million. But this is still only medium-sized in the world of recommenders. This chapter ups the ante again by tackling a larger data set of 130 million preferences in the form of article-to-article links from Wikipedia’s massive corpus.[1] In this data set, the articles are both the users and the items, which also demonstrates how recommenders can be usefully applied, with Mahout, to less ...
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