Merge Mahout item based recommendations results from different algorithms
From the post:
Apache Mahout is a machine learning library that leverages the power of Hadoop to implement machine learning through the MapReduce paradigm. One of the implemented algorithms is collaborative filtering, the most successful recommendation technique to date. The basic idea behind collaborative filtering is to analyze the actions or opinions of users to recommend items similar to the one the user is interacting with.
Similarity isn’t restricted to a particular measure or metric.
How similar is enough to be considered the same?
That is a question topic map designers must answer on a case by case basis.