tweak movielens demo

2 files changed, 3 insertions, 3 deletions

diff --git a/demo/movielens/Makefile b/demo/movielens/Makefile
index f6ef4411..ece8d9e1 100644
--- a/demo/movielens/Makefile
+++ b/demo/movielens/Makefile
@@ -95,9 +95,9 @@ lrqdropout.results: ml-1m.ratings.test.vw ml-1m.ratings.train.vw
 	@echo "* vw --lrq um12 --lrqdropout ...                     *"
 	@echo "******************************************************"
 	@echo
-	@${VW} --loss_function quantile -l 1 -b 24 --passes 100 	\
+	@${VW} --loss_function quantile -l 0.45 -b 24 --passes 100 	\
 	  -k --cache_file $@.cache -d $(word 2,$+) --holdout_off	\
-	  --lrq um12 --lrqdropout --adaptive --invariant -f $@.model
+	  --lrq um14 --lrqdropout --adaptive --invariant -f $@.model
 	@echo "*****************************************************"
 	@echo "* testing low-rank interaction model (with dropout) *"
 	@echo "*****************************************************"
diff --git a/demo/movielens/README.md b/demo/movielens/README.md
index 4b6a49ab..674ca02b 100755
--- a/demo/movielens/README.md
+++ b/demo/movielens/README.md
@@ -35,7 +35,7 @@ You might find a bit of `--l2` regularization improves generalization.
 - `make shootout`: eventually produces three results indicating test MAE (mean absolute error) on movielens-1M for
  - linear: a model without any interactions.  basically this creates a user bias and item bias fit.  this is a surprisingly strong baseline in terms of MAE, but is useless for recommendation as it induces the same item ranking for all users.  It achieves test MAE of 0.731.
  - lrq: the linear model augmented with rank-7 interactions between users and movies, aka, "seven latent factors".  It achieves test MAE of 0.698.  I determined that 7 was the best number to use through experimentation.  The additional `vw` command-line flags vs. the linear model are `--l2 1e-6 --lrq um7`.  Performance is sensitive to the choice of `--l2` regularization strength.
- - lrqdropout: the linear model augmented with rank-12 interactions between users and movies, and trained with dropout.  It achieves test MAE of 0.689.  The additional `vw` command-line flags vs. the linear model are `--lrq um12 --lrqdropout`.
+ - lrqdropout: the linear model augmented with rank-14 interactions between users and movies, and trained with dropout.  It achieves test MAE of 0.688.  The additional `vw` command-line flags vs. the linear model are `--lrq um14 --lrqdropout`.
 - the first time you invoke `make shootout` there is a lot of other output.  invoking it a second time will allow you to just see the cached results.
 
 Details about how `vw` is invoked is in the `Makefile`.