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Building Recommendation Engines by Suresh Kumar Gorakala

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Distributed recommendations using Mahout

Up to now, we have seen how to build recommendation engines in the standalone mode. In most cases, the standalone implementations are very handy and they work quite efficiently in handling a million records provided we supply the dataset format, such as the userID, itemID, and preference triplet.

When the size of the data increases, the standalone mode might not be able to address the requirements. We need to look for ways to handle the enormous amount of data and be able to process the data to build recommendations. One approach is to port our standalone solution to the distributed mode, an example of which is Hadoop platforms.

The porting of the recommender solution to Hadoop is not straight forward, as ...

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