diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..e2b805e
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,15 @@
+language: python
+python:
+  - "2.6"
+  - "2.7"
+  - "3.2"
+  - "3.3"
+virtualenv:
+  system_site_packages: true
+before_install:
+ - sudo apt-get install -qq python-numpy python-scipy
+install:
+# command to install dependencies
+install: "pip install scipy numpy csc-pysparse networkx divisi2"
+# command to run tests
+script: nosetests
diff --git a/README.rst b/README.rst
index 0d0635a..ca218e0 100644
--- a/README.rst
+++ b/README.rst
@@ -12,7 +12,7 @@ Dependencies
 
 **python-recsys** is build on top of `Divisi2`_, with csc-pysparse (Divisi2 also requires `NumPy`_, and uses Networkx).
 
-.. _`Divisi2`: http://csc.media.mit.edu/docs/divisi2/install.html
+.. _`Divisi2`: https://github.com/commonsense/divisi2
 .. _`NumPy`: http://numpy.scipy.org
 
 **python-recsys** also requires `SciPy`_.
@@ -48,6 +48,112 @@ Install
     cd python-recsys
     sudo python setup.py install
 
+Example
+~~~~~~~
+
+1. Load Movielens dataset:
+
+::
+
+    from recsys.algorithm.factorize import SVD
+    svd = SVD()
+    svd.load_data(filename='./data/movielens/ratings.dat', 
+                sep='::', 
+                format={'col':0, 'row':1, 'value':2, 'ids': int})
+
+2. Compute Singular Value Decomposition (SVD), M=U Sigma V^t:
+
+::
+
+    k = 100
+    svd.compute(k=k, 
+                min_values=10, 
+                pre_normalize=None, 
+                mean_center=True, 
+                post_normalize=True, 
+                savefile='/tmp/movielens')
+
+3. Get similarity between two movies:
+
+::
+
+    ITEMID1 = 1    # Toy Story (1995)
+    ITEMID2 = 2355 # A bug's life (1998)
+
+    svd.similarity(ITEMID1, ITEMID2)
+    # 0.67706936677315799
+
+4. Get movies similar to *Toy Story*:
+
+::
+
+    svd.similar(ITEMID1)
+
+    # Returns: <ITEMID, Cosine Similarity Value>
+    [(1,    0.99999999999999978), # Toy Story
+     (3114, 0.87060391051018071), # Toy Story 2
+     (2355, 0.67706936677315799), # A bug's life
+     (588,  0.5807351496754426),  # Aladdin
+     (595,  0.46031829709743477), # Beauty and the Beast
+     (1907, 0.44589398718134365), # Mulan
+     (364,  0.42908159895574161), # The Lion King
+     (2081, 0.42566581277820803), # The Little Mermaid
+     (3396, 0.42474056361935913), # The Muppet Movie
+     (2761, 0.40439361857585354)] # The Iron Giant
+
+5. Predict the rating a user (USERID) would give to a movie (ITEMID):
+
+::
+
+    MIN_RATING = 0.0
+    MAX_RATING = 5.0
+    ITEMID = 1
+    USERID = 1
+
+    svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
+    # Predicted value 5.0 
+
+    svd.get_matrix().value(ITEMID, USERID)
+    # Real value 5.0 
+
+6. Recommend (non-rated) movies to a user:
+
+::
+
+    svd.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False
+
+    # Returns: <ITEMID, Predicted Rating>
+    [(2905, 5.2133848204673416), # Shaggy D.A., The
+     (318,  5.2052108435956033), # Shawshank Redemption, The
+     (2019, 5.1037438278755474), # Seven Samurai (The Magnificent Seven)
+     (1178, 5.0962756861447023), # Paths of Glory (1957)
+     (904,  5.0771405690055724), # Rear Window (1954)
+     (1250, 5.0744156653222436), # Bridge on the River Kwai, The
+     (858,  5.0650911066862907), # Godfather, The
+     (922,  5.0605327279819408), # Sunset Blvd.
+     (1198, 5.0554543765500419), # Raiders of the Lost Ark
+     (1148, 5.0548789542105332)] # Wrong Trousers, The
+
+7. Which users should *see* Toy Story? (e.g. which users -that have not rated Toy
+   Story- would give it a high rating?)
+
+::
+
+    svd.recommend(ITEMID)
+
+    # Returns: <USERID, Predicted Rating>
+    [(283,  5.716264440514446),
+     (3604, 5.6471765418323141),
+     (5056, 5.6218800339214496),
+     (446,  5.5707524860615738),
+     (3902, 5.5494529168484652),
+     (4634, 5.51643364021289),
+     (3324, 5.5138903299082802),
+     (4801, 5.4947999354188548),
+     (1131, 5.4941438045650068),
+     (2339, 5.4916048051511659)]
+    
+
 Documentation
 ~~~~~~~~~~~~~
 
diff --git a/doc/source/installation.rst b/doc/source/installation.rst
index 4c28d08..836aa15 100644
--- a/doc/source/installation.rst
+++ b/doc/source/installation.rst
@@ -22,7 +22,7 @@ To install the dependencies do something like this (Ubuntu):
     sudo pip install csc-pysparse networkx divisi2
 
     # If you don't have pip installed 
-    # (i.e. the last command, sudo pip install divisi2 csc-pysparse, fails)
+    # (i.e. the last command, sudo pip install, fails)
     # then do:
     # sudo easy_install csc-pysparse
     # sudo easy_install networkx
diff --git a/recsys/__init__.py b/recsys/__init__.py
index 639ffd7..e51984e 100644
--- a/recsys/__init__.py
+++ b/recsys/__init__.py
@@ -1,7 +1,7 @@
 __name__ = "recsys"
 __doc__ = "python-recsys: A simple python recommender system"
 __author__ = "Oscar Celma"
-__version__ = "0.1"
+__version__ = "0.2"
 __license__ = "GPL"
 __maintainer__ = "Oscar Celma"
 __email__ = "ocelma@gmail.com"