Fixing README.md

README.md

@@ -3,27 +3,24 @@ LightFM
 
 An implementation of Factorization Machines. Intended to be educational.
 
-Current implementation does not use any fancy tricks to speed up the prediction
-and learning phase.
-
-Currently, LightFM does not take any command line arguments. You have to modify
-the source code if you want to change the behavior of the program in any way.
+Currently supports naive feature vectorization using maps and feature hashing
+a.k.a. hashing tricks.
 
 Weights are optimized using stochastic gradient descent. It only looks at a
 single training example and does not work in full-batch or mini-batch mode.
 
 ## Data
 
-LightFM follows LibSVM format.
+LightFM expects the following input format.
 
     target feature:weight feature:weight
 
 First term is the target which is followed by features. An example is given
 below.
 
-    3.5 54081:1 843:1
-    3 49752:1 324:1
-    4.5 60777:1 1011:1
+    3.5 p54081:1 i843:1
+    3 p49752:1 i324:1
+    4.5 p60777:1 i1011:1
 
 ## Build
 
@@ -35,15 +32,16 @@ below.
 
 ## Execution
 
-Currently, LightFM does not take any command line arguments. You have to modify
-the source code to change the input path.
+LightFM takes various command line arguments. You need to specify the path for
+training data and test data.
 
-    make run
+    bin/lightfm -d <training data path> -t <test data path>
 
-or,
+Other arguments can be inspected by `--help` command.
 
-    bin/lightfm
+    bin/lightfm --help
 
 ## Reference
 
 - Steffen Rendle (2010): [Factorization Machines](http://www.inf.uni-konstanz.de/~rendle/pdf/Rendle2010FM.pdf), in Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia.
+- Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). [Feature Hashing for Large Scale Multitask Learning](http://arxiv.org/pdf/0902.2206.pdf) (ICML)