This module is designed to provide personalized recommendations to users based on their voting patterns. It uses a weight sum algorithm common in memory based collaborative filtering to do this. All recommendations are cached in the cre_recommendations_table.

The module uses Views 2.0 to render pages to users. And it allows the use of any Similarity Object

You should be aware that this module does make use of the database. The tables can grow. This is really done to prevent having to calculate the predicted score on query which would make the query time unacceptable.

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