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You can get pearson coefficient top matches with:

recommendations.topMatches(recommendations.critics,'Mark',n=11)

Where 'Mark' is the name of the critic and n= number of comparison

You can get weighted film recommendations with:

recommendations.getRecommendations(recommendaitons.critics,'Mark')

Where 'Mark' is the name of the critic you want recommendations for

You can use Pearson coefficient with:

recommendations.sim_pearson(recommendations.critics,'Mark', 'Tom')

You can use Euclidean Distance Score with:

recommendations.sim_distance(recommendations.critics,'Mark', 'Tom')