From 16967fe9e33bf43fee66e63c1eef90ca445b635d Mon Sep 17 00:00:00 2001
From: Max Sperlich <max.sperlich@livingsocial.com>
Date: Tue, 29 Apr 2014 18:10:06 -0400
Subject: [PATCH 1/3] Fixed typo in regnormal code

---
 toolkits/collaborative_filtering/als_tensor.cpp | 2 +-
 toolkits/collaborative_filtering/pmf.cpp        | 2 +-
 toolkits/collaborative_filtering/sparse_als.cpp | 2 +-
 toolkits/collaborative_filtering/wals.cpp       | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/toolkits/collaborative_filtering/als_tensor.cpp b/toolkits/collaborative_filtering/als_tensor.cpp
index 6262f2ea..44f9822e 100644
--- a/toolkits/collaborative_filtering/als_tensor.cpp
+++ b/toolkits/collaborative_filtering/als_tensor.cpp
@@ -134,7 +134,7 @@ struct ALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeData
     }
     double regularization = lambda;
     if (regnormal)
-      lambda *= vertex.num_edges();
+      regularization *= vertex.num_edges();
     for(int i=0; i < D; i++) XtX(i,i) += regularization;
 
     // Solve the least squares problem with eigen using Cholesky decomposition
diff --git a/toolkits/collaborative_filtering/pmf.cpp b/toolkits/collaborative_filtering/pmf.cpp
index a0b29b66..8b2a104f 100644
--- a/toolkits/collaborative_filtering/pmf.cpp
+++ b/toolkits/collaborative_filtering/pmf.cpp
@@ -329,7 +329,7 @@ struct PMFVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeData
 
     double regularization = lambda;
     if (regnormal)
-      lambda *= vertex.num_edges();
+      regularization *= vertex.num_edges();
     for(int i=0; i < D; i++) XtX(i,i) += regularization;
 
     // Solve the least squares problem with eigen using Cholesky decomposition
diff --git a/toolkits/collaborative_filtering/sparse_als.cpp b/toolkits/collaborative_filtering/sparse_als.cpp
index a2443f7e..3be1342f 100644
--- a/toolkits/collaborative_filtering/sparse_als.cpp
+++ b/toolkits/collaborative_filtering/sparse_als.cpp
@@ -127,7 +127,7 @@ struct ALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeData
 
     double regularization = lambda;
     if (regnormal)
-      lambda *= vertex.num_edges();
+      regularization *= vertex.num_edges();
     for(int i=0; i < D; i++) XtX(i,i) += regularization;
 
 
diff --git a/toolkits/collaborative_filtering/wals.cpp b/toolkits/collaborative_filtering/wals.cpp
index f94e1e23..1cbae954 100644
--- a/toolkits/collaborative_filtering/wals.cpp
+++ b/toolkits/collaborative_filtering/wals.cpp
@@ -127,7 +127,7 @@ struct WALSVerticesInMemProgram : public GraphChiProgram<VertexDataType, EdgeDat
     }
     double regularization = lambda;
     if (regnormal)
-      lambda *= vertex.num_edges();
+      regularization *= vertex.num_edges();
     for(int i=0; i < D; i++) XtX(i,i) += regularization;
 
     // Solve the least squares problem with eigen using Cholesky decomposition

From 162a6ed75e3152f4003f925dae47144d0e97aa92 Mon Sep 17 00:00:00 2001
From: Max Sperlich <max.sperlich@livingsocial.com>
Date: Mon, 5 May 2014 11:30:33 -0400
Subject: [PATCH 2/3] LibFM: fixed typo in load last_item_bias code

last_item_bias file is saved with .mm extension, but loaded with .m
---
 toolkits/collaborative_filtering/libfm.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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];

From 07e530e3f48279876abf3f5a83ecd63d37190e32 Mon Sep 17 00:00:00 2001
From: Max Sperlich <max.sperlich@livingsocial.com>
Date: Wed, 14 May 2014 16:52:38 -0400
Subject: [PATCH 3/3] ALS/WALS: use rankUpdate() to update XtX

This optimized calculation gives around a 2X speedup on the small netflix set
in my testing (D=20)
---
 toolkits/collaborative_filtering/als.cpp  | 2 +-
 toolkits/collaborative_filtering/wals.cpp | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

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/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;