diff --git a/code/README.md b/code/README.md
deleted file mode 100644
index 6e0ad3c..0000000
--- a/code/README.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# Xmap
-
-Xmap is the implementation of the paper `What You Might Like To Read After Watching Interstellar`. The system is built upon Apache Spark, and written by Python.
-
-
-## Getting Started
-
-### Prerequisites
-
-You should have `Python 3`, `Numpy 1.10.4`, `Apache Spark 1.6.1` installed on your computer.
-
-#### Installing
-Please refer to
-[Anaconda](https://www.continuum.io/downloads), [Apache Spark](http://spark.apache.org/) for installization.
-
-#### Installing (Docker)
-We also provide a docker image for your convenience.
-
-If you do not have `docker` and `docker-compose` installed on your computer, please check the [official configuration guidance](https://www.docker.com/products/overview) for help.
-Otherwise, open your console and go to the top-level folder `platform`, and type the command below to setup the corresponding docker image.
-
-```
-docker-compose build
-```
-
-## Build
-Once you modify the scripts in `xmap` folder, you should rebuild the package through the following command:
-
-```
-python setup.py install
-```
-
-You will find an egg file that located in `dist/xmap-0.1.0-py3.5.egg`, and you will use this file for your application.
-
-## Running the tests
-### Prerequisites
-Xmap use [Amazon Dataset](https://snap.stanford.edu/data/web-Amazon.html) for its experiment. For current implementation, the input data follows the following format:
-`userid itemid rating time`, seperated by `\t`.
-
-### Run Xmap
-Here we provide two examples `twodomain_demo.py` and `multidomain_demo.py`, and you can set your parameters in the file `parameters.yaml`.
-
-Note that the scipt should run successfully under provided docker image. Please check your local paramesters (e.g., directory path) when working on your own case.
-
-#### Run Xmap locally on 4 cores with docker
-A simple example of how to run script locally.
-
-```
-spark-submit --master local[4] \
-    --py-files dist/xmap-0.1.0-py3.5.egg twodomain_demo.py
-```
-
-#### Distributed case on a Cluster
-A simple example of how to run script on a Cluster.
-
-```
-spark-submit 	--py-files xmap-0.1.0-py3.5.egg \
-				--num-executors 30 --executor-cores 3 --executor-memory 12g \
-				--driver-memory 12g --driver-cores 4 twodomain_demo.py
-```
-
-## Advance Usage
-### Introduce other methodology
-Xmap can be easily extend to matrix factorization verision by using built-in MLlib in Spark. Please check a simplified example below:
-
-```
-from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.ml.recommendation import ALS
-from xmap.core import *
-
-# use component in xmap to build alterEgo profile.
-sourceRDD = baseliner_clean_data_pipeline(...)
-targetRDD = baseliner_clean_data_pipeline(...)
-trainRDD, testRDD = baseliner_split_data_pipeline(...)
-item2item_simRDD = baseliner_calculate_sim_pipeline(...)
-extendedsimRDD = extender_pipeline(...)
-alterEgo_profileRDD = generator_pipeline(...)
-
-# build MLlib to do matrix factorization
-
-als = ALS(...)
-model = als.fit(alterEgo_profileRDD)
-predictions = model.transform(testRDD)
-evaluator = RegressionEvaluator(...)
-rmse = evaluator.evaluate(predictions)
-print("Root-mean-square error = " + str(rmse))
-```