diff --git a/toolkits/collaborative_filtering/als.cpp b/toolkits/collaborative_filtering/als.cpp
index ff9de205..5c791ffd 100644
--- a/toolkits/collaborative_filtering/als.cpp
+++ b/toolkits/collaborative_filtering/als.cpp
@@ -131,7 +131,7 @@ struct ALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeData
       float observation = vertex.edge(e)->get_data();                
       vertex_data & nbr_latent = latent_factors_inmem[vertex.edge(e)->vertex_id()];
       Xty += nbr_latent.pvec * observation;
-      XtX.triangularView<Eigen::Upper>() += nbr_latent.pvec * nbr_latent.pvec.transpose();
+      XtX.selfadjointView<Eigen::Upper>().rankUpdate(nbr_latent.pvec);
       if (compute_rmse) {
         double prediction;
         rmse_vec[omp_get_thread_num()] += als_predict(vdata, nbr_latent, observation, prediction);
diff --git a/toolkits/collaborative_filtering/libfm.cpp b/toolkits/collaborative_filtering/libfm.cpp
index 0aaa5408..f3757827 100644
--- a/toolkits/collaborative_filtering/libfm.cpp
+++ b/toolkits/collaborative_filtering/libfm.cpp
@@ -314,7 +314,7 @@ int main(int argc, const char ** argv) {
     vec user_bias =      load_matrix_market_vector(training +"_U_bias.mm", false, true);
     vec item_bias =      load_matrix_market_vector(training +"_V_bias.mm", false, true);
     vec time_bias =      load_matrix_market_vector(training+ "_T_bias.mm", false, true);
-    vec last_item_bias = load_matrix_market_vector(training+"_L_bias.m", false, true);
+    vec last_item_bias = load_matrix_market_vector(training+"_L_bias.mm", false, true);
     for (uint i=0; i<M+N+K+M; i++){
       if (i < M)
 	latent_factors_inmem[i].bias = user_bias[i];
diff --git a/toolkits/collaborative_filtering/wals.cpp b/toolkits/collaborative_filtering/wals.cpp
index 1cbae954..7d50af0a 100644
--- a/toolkits/collaborative_filtering/wals.cpp
+++ b/toolkits/collaborative_filtering/wals.cpp
@@ -119,7 +119,7 @@ struct WALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeDat
       const edge_data & edge = vertex.edge(e)->get_data();                
       vertex_data & nbr_latent = latent_factors_inmem[vertex.edge(e)->vertex_id()];
       Xty += nbr_latent.pvec * edge.weight * edge.time;      
-      XtX.triangularView<Eigen::Upper>() += nbr_latent.pvec * nbr_latent.pvec.transpose() * edge.time;
+      XtX.selfadjointView<Eigen::Upper>().rankUpdate(nbr_latent.pvec, edge.time);
       if (compute_rmse) {
         double prediction;
         rmse_vec[omp_get_thread_num()] += wals_predict(vdata, nbr_latent, edge.weight, prediction) * edge.time;