diff --git a/src/mlpack/methods/cf/cf_main.cpp b/src/mlpack/methods/cf/cf_main.cpp
index 8aba9a8673..add5df5f90 100644
--- a/src/mlpack/methods/cf/cf_main.cpp
+++ b/src/mlpack/methods/cf/cf_main.cpp
@@ -80,6 +80,18 @@ PROGRAM_INFO("Collaborative Filtering", "This program performs collaborative "
     " - 'SVDCompleteIncremental' -- SVD complete incremental learning\n"
     " - 'BiasSVD' -- Bias SVD using a SGD optimizer\n"
     " - 'SVDPP' -- SVD++ using a SGD optimizer\n"
+    "\n\n"
+    "The following neighbor search algorithms can be specified via" +
+    " the " + PRINT_PARAM_STRING("neighbor_search") + " parameter:"
+    " - 'Cosine'  -- Cosine Search Algorithm\n"
+    " - 'Euclidean'  -- Euclidean Search Algorithm\n"
+    " - 'Pearson'  -- Pearson Search Algorithm\n"
+    "\n\n"
+    "The following weight interpolation algorithms can be specified via" +
+    " the " + PRINT_PARAM_STRING("interpolation") + " parameter:"
+    " - 'Average'  -- Average Interpolation Algorithm\n"
+    " - 'Regression'  -- Regression Interpolation Algorithm\n"
+    " - 'Similarity'  -- Similarity Interpolation Algorithm\n"
     "\n"
     "A trained model may be saved to with the " +
     PRINT_PARAM_STRING("output_model") + " output parameter."
@@ -134,19 +146,21 @@ PARAM_INT_IN("recommendations", "Number of recommendations to generate for each"
 
 PARAM_INT_IN("seed", "Set the random seed (0 uses std::time(NULL)).", "s", 0);
 
-//  Interpolation and Neighbour Search Algorithms
+//  Interpolation and Neighbor Search Algorithms
 PARAM_STRING_IN("interpolation", "Algorithm used for weight interpolation.",
     "i", "Average");
 
-PARAM_STRING_IN("neighbour_search", "Algorithm used for neighbour search.",
+PARAM_STRING_IN("neighbor_search", "Algorithm used for neighbor search.",
     "f", "Euclidean");
 
 void ComputeRecommendations(CFModel* cf,
                             const size_t numRecs,
                             arma::Mat<size_t>& recommendations)
 {
-  const string ns_algo = CLI::GetParam<string>("neighbour_search");
-  const string iw_algo = CLI::GetParam<string>("interpolation");
+  //  Taking alternatives
+  const string neighborSearchAlgorithm = CLI::GetParam<string>
+      ("neighbor_search");
+  const string interpolationAlgorithm = CLI::GetParam<string>("interpolation");
 
   // Reading users.
   if (CLI::HasParam("query"))
@@ -157,142 +171,134 @@ void ComputeRecommendations(CFModel* cf,
     if (users.n_rows > 1)
       users = users.t();
     if (users.n_rows > 1)
-      Log::Fatal << "List of query users must be one-dimensional!" << std::endl;
+      Log::Fatal << "List of query users must be one-dimensional!"
+                 << std::endl;
 
-    Log::Info << "Generating recommendations for " << users.n_elem << " users."
-        << endl;
+    Log::Info << "Generating recommendations for "
+              << users.n_elem << " users."
+              << endl;
 
-    //  Making All the possible paths
-    if (ns_algo == "Cosine")
+    //  All possible alternatives for Recommendations
+    if (neighborSearchAlgorithm == "Cosine")
     {
-        if (iw_algo == "Average")
-        {
-            cf->GetRecommendations<CosineSearch,
-              AverageInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
-        else if (iw_algo == "Regression")
-        {
-            cf->GetRecommendations<CosineSearch,
-              RegressionInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
-        else if (iw_algo == "Similarity")
-        {
-            cf->GetRecommendations<CosineSearch,
-              SimilarityInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->GetRecommendations<CosineSearch, AverageInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->GetRecommendations<CosineSearch, RegressionInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->GetRecommendations<CosineSearch, SimilarityInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
     }
-    else if (ns_algo == "Euclidean")
+    else if (neighborSearchAlgorithm == "Euclidean")
     {
-        if (iw_algo == "Average")
-        {
-            cf->GetRecommendations<EuclideanSearch,
-              AverageInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
-        else if (iw_algo == "Regression")
-        {
-            cf->GetRecommendations<EuclideanSearch,
-              RegressionInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
-        else if (iw_algo == "Similarity")
-        {
-            cf->GetRecommendations<EuclideanSearch,
-              SimilarityInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->GetRecommendations<EuclideanSearch, AverageInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->GetRecommendations<EuclideanSearch, RegressionInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->GetRecommendations<EuclideanSearch, SimilarityInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
     }
-    else if (ns_algo == "Pearson")
+    else if (neighborSearchAlgorithm == "Pearson")
     {
-        if (iw_algo == "Average")
-        {
-            cf->GetRecommendations<PearsonSearch,
-              AverageInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
-        else if (iw_algo == "Regression")
-        {
-            cf->GetRecommendations<PearsonSearch,
-              RegressionInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
-        else if (iw_algo == "Similarity")
-        {
-            cf->GetRecommendations<PearsonSearch,
-              SimilarityInterpolation>(numRecs, recommendations,
-              users.row(0).t());
-        }
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->GetRecommendations<PearsonSearch, AverageInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->GetRecommendations<PearsonSearch, RegressionInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->GetRecommendations<PearsonSearch, SimilarityInterpolation>
+            (numRecs, recommendations, users.row(0).t());
+      }
     }
   }
   else
   {
     Log::Info << "Generating recommendations for all users." << endl;
-      if (ns_algo == "Cosine")
+    if (neighborSearchAlgorithm == "Cosine")
+    {
+      if (interpolationAlgorithm == "Average")
       {
-          if (iw_algo == "Average")
-          {
-              cf->GetRecommendations<CosineSearch,
-                AverageInterpolation>(numRecs, recommendations);
-          }
-          else if (iw_algo == "Regression")
-          {
-              cf->GetRecommendations<CosineSearch,
-                RegressionInterpolation>(numRecs, recommendations);
-          }
-          else if (iw_algo == "Similarity")
-          {
-              cf->GetRecommendations<CosineSearch,
-                SimilarityInterpolation>(numRecs, recommendations);
-          }
+        cf->GetRecommendations<CosineSearch, AverageInterpolation>
+            (numRecs, recommendations);
       }
-      else if (ns_algo == "Euclidean")
+      else if (interpolationAlgorithm == "Regression")
       {
-          if (iw_algo == "Average")
-          {
-              cf->GetRecommendations<EuclideanSearch,
-                AverageInterpolation>(numRecs, recommendations);
-          }
-          else if (iw_algo == "Regression")
-          {
-              cf->GetRecommendations<EuclideanSearch,
-                RegressionInterpolation>(numRecs, recommendations);
-          }
-          else if (iw_algo == "Similarity")
-          {
-              cf->GetRecommendations<EuclideanSearch,
-                SimilarityInterpolation>(numRecs, recommendations);
-          }
+        cf->GetRecommendations<CosineSearch, RegressionInterpolation>
+            (numRecs, recommendations);
       }
-      else if (ns_algo == "Pearson")
+      else if (interpolationAlgorithm == "Similarity")
       {
-          if (iw_algo == "Average")
-          {
-              cf->GetRecommendations<PearsonSearch,
-                AverageInterpolation>(numRecs, recommendations);
-          }
-          else if (iw_algo == "Regression")
-          {
-              cf->GetRecommendations<PearsonSearch,
-                RegressionInterpolation>(numRecs, recommendations);
-          }
-          else if (iw_algo == "Similarity")
-          {
-              cf->GetRecommendations<PearsonSearch,
-                SimilarityInterpolation>(numRecs, recommendations);
-          }
+        cf->GetRecommendations<CosineSearch, SimilarityInterpolation>
+            (numRecs, recommendations);
       }
+    }
+    else if (neighborSearchAlgorithm == "Euclidean")
+    {
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->GetRecommendations<EuclideanSearch, AverageInterpolation>
+            (numRecs, recommendations);
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->GetRecommendations<EuclideanSearch, RegressionInterpolation>
+            (numRecs, recommendations);
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->GetRecommendations<EuclideanSearch, SimilarityInterpolation>
+            (numRecs, recommendations);
+      }
+    }
+    else if (neighborSearchAlgorithm == "Pearson")
+    {
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->GetRecommendations<PearsonSearch, AverageInterpolation>
+            (numRecs, recommendations);
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->GetRecommendations<PearsonSearch, RegressionInterpolation>
+            (numRecs, recommendations);
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->GetRecommendations<PearsonSearch, SimilarityInterpolation>
+            (numRecs, recommendations);
+      }
+    }
   }
 }
 
 void ComputeRMSE(CFModel* cf)
 {
-  //    Interpolation and Neighbour Search
-
-  const string ns_algo = CLI::GetParam<string>("neighbour_search");
-  const string iw_algo = CLI::GetParam<string>("interpolation");
+  //    Interpolation and Neighbor Search
+  const string neighborSearchAlgorithm = CLI::GetParam<string>("neighbor_search");
+  const string interpolationAlgorithm = CLI::GetParam<string>("interpolation");
 
   // Now, compute each test point.
   arma::mat testData = std::move(CLI::GetParam<arma::mat>("test"));
@@ -308,59 +314,59 @@ void ComputeRMSE(CFModel* cf)
   // Now compute the RMSE.
   arma::vec predictions;
 
-  if (ns_algo == "Cosine")
+  if (neighborSearchAlgorithm == "Cosine")
     {
-        if (iw_algo == "Average")
-        {
-            cf->Predict<CosineSearch,
-              AverageInterpolation>(combinations, predictions);
-        }
-        else if (iw_algo == "Regression")
-        {
-            cf->Predict<CosineSearch,
-              RegressionInterpolation>(combinations, predictions);
-        }
-        else if (iw_algo == "Similarity")
-        {
-            cf->Predict<CosineSearch,
-              SimilarityInterpolation>(combinations, predictions);
-        }
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->Predict<CosineSearch, AverageInterpolation>
+            (combinations, predictions);
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->Predict<CosineSearch, RegressionInterpolation>
+            (combinations, predictions);
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->Predict<CosineSearch, SimilarityInterpolation>
+            (combinations, predictions);
+      }
     }
-  else if (ns_algo == "Euclidean")
+  else if (neighborSearchAlgorithm == "Euclidean")
     {
-        if (iw_algo == "Average")
-        {
-            cf->Predict<EuclideanSearch,
-              AverageInterpolation>(combinations, predictions);
-        }
-        else if (iw_algo == "Regression")
-        {
-            cf->Predict<EuclideanSearch,
-              RegressionInterpolation>(combinations, predictions);
-        }
-        else if (iw_algo == "Similarity")
-        {
-            cf->Predict<EuclideanSearch,
-              SimilarityInterpolation>(combinations, predictions);
-        }
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->Predict<EuclideanSearch, AverageInterpolation>
+            (combinations, predictions);
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->Predict<EuclideanSearch, RegressionInterpolation>
+            (combinations, predictions);
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->Predict<EuclideanSearch, SimilarityInterpolation>
+            (combinations, predictions);
+      }
     }
-  else if (ns_algo == "Pearson")
+  else if (neighborSearchAlgorithm == "Pearson")
     {
-        if (iw_algo == "Average")
-        {
-            cf->Predict<PearsonSearch,
-              AverageInterpolation>(combinations, predictions);
-        }
-        else if (iw_algo == "Regression")
-        {
-            cf->Predict<PearsonSearch,
-              RegressionInterpolation>(combinations, predictions);
-        }
-        else if (iw_algo == "Similarity")
-        {
-            cf->Predict<PearsonSearch,
-              SimilarityInterpolation>(combinations, predictions);
-        }
+      if (interpolationAlgorithm == "Average")
+      {
+        cf->Predict<PearsonSearch, AverageInterpolation>
+            (combinations, predictions);
+      }
+      else if (interpolationAlgorithm == "Regression")
+      {
+        cf->Predict<PearsonSearch, RegressionInterpolation>
+            (combinations, predictions);
+      }
+      else if (interpolationAlgorithm == "Similarity")
+      {
+        cf->Predict<PearsonSearch, SimilarityInterpolation>
+            (combinations, predictions);
+      }
     }
 
   // Compute the root of the sum of the squared errors, divide by the number of
@@ -482,12 +488,12 @@ static void mlpackMain()
       "SVDIncompleteIncremental", "SVDCompleteIncremental", "RegSVD",
       "RandSVD", "BiasSVD", "SVDPP" }, true, "unknown algorithm");
 
-    //    Validate the interpolation and neighbour_search policy
+    //    Validate the interpolation and neighbor_search policy
   RequireParamInSet<string>("interpolation", { "Average",
       "Regression", "Similarity" }, true, "unknown interpolation algorithm");
 
-  RequireParamInSet<string>("neighbour_search", { "Cosine",
-      "Euclidean", "Pearson" }, true, "unknown neighbour search algorithm");
+  RequireParamInSet<string>("neighbor_search", { "Cosine",
+      "Euclidean", "Pearson" }, true, "unknown neighbor search algorithm");
 
   ReportIgnoredParam({{ "iteration_only_termination", true }}, "min_residue");
 
diff --git a/src/mlpack/methods/cf/cf_model.hpp b/src/mlpack/methods/cf/cf_model.hpp
index ae568e371d..ef1ebac214 100644
--- a/src/mlpack/methods/cf/cf_model.hpp
+++ b/src/mlpack/methods/cf/cf_model.hpp
@@ -54,7 +54,7 @@ class GetValueVisitor : public boost::static_visitor<void*>
  * PredictVisitor uses the CFType object to make predictions on the given
  * combinations of users and items.
  */
-template <typename NeighbourSearchPolicy,
+template <typename NeighborSearchPolicy,
           typename InterpolationPolicy>
 class PredictVisitor : public boost::static_visitor<void>
 {
@@ -78,7 +78,7 @@ class PredictVisitor : public boost::static_visitor<void>
  * RecommendationVisitor uses the CFType object to get recommendations for the
  * given users.
  */
-template <typename NeighbourSearchPolicy,
+template <typename NeighborSearchPolicy,
           typename InterpolationPolicy>
 class RecommendationVisitor : public boost::static_visitor<void>
 {
@@ -146,21 +146,21 @@ class CFModel
              const bool mit);
 
   //! Make predictions.
-  template <typename NeighbourSearchPolicy,
+  template <typename NeighborSearchPolicy,
             typename InterpolationPolicy>
   void Predict(const arma::Mat<size_t>& combinations,
                arma::vec& predictions);
 
   //! Compute recommendations for query users.
-  template<typename NeighbourSearchPolicy,
+  template<typename NeighborSearchPolicy,
            typename InterpolationPolicy>
   void GetRecommendations(const size_t numRecs,
                           arma::Mat<size_t>& recommendations,
                           const arma::Col<size_t>& users);
 
   //! Compute recommendations for all users.
-  template<typename NeighbourSearchPolicy,
-          typename InterpolationPolicy>
+  template<typename NeighborSearchPolicy,
+           typename InterpolationPolicy>
   void GetRecommendations(const size_t numRecs,
                           arma::Mat<size_t>& recommendations);
 
diff --git a/src/mlpack/methods/cf/cf_model_impl.hpp b/src/mlpack/methods/cf/cf_model_impl.hpp
index b503f5f868..aff6beeb11 100644
--- a/src/mlpack/methods/cf/cf_model_impl.hpp
+++ b/src/mlpack/methods/cf/cf_model_impl.hpp
@@ -34,19 +34,19 @@ void* GetValueVisitor::operator()(CFType<DecompositionPolicy>* c) const
   return (void*) c;
 }
 
-template <typename NeighbourSearchPolicy,
+template <typename NeighborSearchPolicy,
           typename InterpolationPolicy>
-PredictVisitor<NeighbourSearchPolicy,InterpolationPolicy>::PredictVisitor(
+PredictVisitor<NeighborSearchPolicy, InterpolationPolicy>::PredictVisitor(
     const arma::Mat<size_t>& combinations,
     arma::vec& predictions) :
     combinations(combinations),
     predictions(predictions)
 { }
 
-template <typename NeighbourSearchPolicy,
+template <typename NeighborSearchPolicy,
           typename InterpolationPolicy>
 template<typename DecompositionPolicy>
-void PredictVisitor<NeighbourSearchPolicy, InterpolationPolicy>
+void PredictVisitor<NeighborSearchPolicy, InterpolationPolicy>
         ::operator()(CFType<DecompositionPolicy>* c) const
 {
   if (!c)
@@ -55,13 +55,13 @@ void PredictVisitor<NeighbourSearchPolicy, InterpolationPolicy>
     return;
   }
 
-  c->template Predict<NeighbourSearchPolicy,
+  c->template Predict<NeighborSearchPolicy,
     InterpolationPolicy>(combinations, predictions);
 }
 
-template <typename NeighbourSearchPolicy,
+template <typename NeighborSearchPolicy,
           typename InterpolationPolicy>
-RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
+RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
         ::RecommendationVisitor(
     const size_t numRecs,
     arma::Mat<size_t>& recommendations,
@@ -73,10 +73,10 @@ RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
     usersGiven(usersGiven)
 { }
 
-template <typename NeighbourSearchPolicy,
-        typename InterpolationPolicy>
+template <typename NeighborSearchPolicy,
+          typename InterpolationPolicy>
 template<typename DecompositionPolicy>
-void RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
+void RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
         ::operator()(CFType<DecompositionPolicy>* c) const
 {
   if (!c)
@@ -86,11 +86,11 @@ void RecommendationVisitor<NeighbourSearchPolicy, InterpolationPolicy>
   }
 
   if (usersGiven)
-    c->template GetRecommendations<NeighbourSearchPolicy,
-      InterpolationPolicy>(numRecs, recommendations, users);
+    c->template GetRecommendations<NeighborSearchPolicy, InterpolationPolicy>
+        (numRecs, recommendations, users);
   else
-    c->template GetRecommendations<NeighbourSearchPolicy,
-      InterpolationPolicy>(numRecs, recommendations);
+    c->template GetRecommendations<NeighborSearchPolicy, InterpolationPolicy>
+        (numRecs, recommendations);
 }
 
 CFModel::~CFModel()
@@ -117,37 +117,37 @@ void CFModel::Train(const MatType& data,
 }
 
 //! Make predictions.
-template <typename NeighbourSearchPolicy,
+template <typename NeighborSearchPolicy,
           typename InterpolationPolicy>
 void CFModel::Predict(const arma::Mat<size_t>& combinations,
                       arma::vec& predictions)
 {
-  PredictVisitor<NeighbourSearchPolicy,
-    InterpolationPolicy> predict(combinations, predictions);
+  PredictVisitor<NeighborSearchPolicy, InterpolationPolicy>
+      predict(combinations, predictions);
   boost::apply_visitor(predict, cf);
 }
 
 //! Compute recommendations for queried users.
-template<typename NeighbourSearchPolicy,
-        typename InterpolationPolicy>
+template<typename NeighborSearchPolicy,
+         typename InterpolationPolicy>
 void CFModel::GetRecommendations(const size_t numRecs,
                                  arma::Mat<size_t>& recommendations,
                                  const arma::Col<size_t>& users)
 {
-  RecommendationVisitor<NeighbourSearchPolicy,
-    InterpolationPolicy> recommendation(numRecs, recommendations, users, true);
+  RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
+      recommendation(numRecs, recommendations, users, true);
   boost::apply_visitor(recommendation, cf);
 }
 
 //! Compute recommendations for all users.
-template<typename NeighbourSearchPolicy,
-        typename InterpolationPolicy>
+template<typename NeighborSearchPolicy,
+         typename InterpolationPolicy>
 void CFModel::GetRecommendations(const size_t numRecs,
                                  arma::Mat<size_t>& recommendations)
 {
   arma::Col<size_t> users;
-  RecommendationVisitor<NeighbourSearchPolicy,
-    InterpolationPolicy> recommendation(numRecs, recommendations, users, false);
+  RecommendationVisitor<NeighborSearchPolicy, InterpolationPolicy>
+      recommendation(numRecs, recommendations, users, false);
   boost::apply_visitor(recommendation, cf);
 }