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Building Recommendation Engines by Suresh Kumar Gorakala

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Nearest neighborhood-based recommendation engines

As the name suggests, neighborhood-based recommender systems considers the preferences or likes of the user community or users of the neighborhood of an active user before making suggestions or recommendations to the active user. The idea for neighborhood-based recommenders is very simple: given the ratings of a user, find all the users similar to the active user who had similar preferences in the past and then make predictions regarding all unknown products that the active user has not rated but are being rated in their neighborhood:

Nearest neighborhood-based recommendation engines

While considering the preferences or tastes of neighbors, we first ...

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