Updated recommenders example (apache#13041)

* initial modification recommender

* Recommender updates

* fix notebooks

* Update README.md

* trigger build

* Update README.md

* Retrigger build

example/recommenders/README.md

@@ -1,46 +1,28 @@
-# Recommender Systems with Sparse Data
+# Recommender Systems
+
 
 This directory has a set of examples of how to build various kinds of recommender systems
-using MXNet.  It also includes a set of tools for using sparse data.
+using MXNet. The sparsity of user / item data is handled through the embedding layers that accept
+indices as input rather than one-hot encoded vectors.
+
 
 ## Examples
 
 The examples are driven by notebook files.
 
-* [Matrix Factorization part 1: linear and non-linear models](demo1-MF.ipynb)
-* [Matrix Factorization part 2: overfitting and deep ResNet](demo1-MF2-fancy.ipynb)
-* [Binary classification with negative sampling](demo2-binary.ipynb)
-* [Deep Structured Semantic Model (DSSM) for content-based recommendations](demo3-dssm.ipynb)
-
-## Prerequisite
-
-The plotting functionality in the above examples requires ```0.12.2``` version of ```Bokeh``` package. The plotting functionality throws following error when a different Bokeh version is loaded.
-```bash
-ValueError: PATCH-DOC message requires at least one event
-```
-
-## Re-usable code
+* [Matrix Factorization: linear and non-linear models](demo1-MF.ipynb)
+* [Deep Structured Semantic Model (DSSM) for content-based recommendations](demo2-dssm.ipynb)
 
-These examples use and demonstrate a number of layers and other tools that can be used outside of these examples.  They are all available from the [`recotools`](recotools.py) package.
 
 ### Negative Sampling
 
-* `NegativeSamplingDataIter` 
-
-### Loss Layers
-
-* `CosineLoss`
-* `CrossEntropyLoss`
-
-### Sparse Data Projection layers
-
-* `SparseRandomProjection`
-* `SparseBagOfWordProjection`
+* A previous version of this example had an example of negative sampling. For example of negative sampling, please refer to:
+    [Gluon NLP Sampled Block](https://github.com/dmlc/gluon-nlp/blob/master/gluonnlp/model/sampled_block.py)
+
 
 ## Acknowledgements
 
 Thanks to [xlvector](https://github.com/xlvector/) for the first Matrix Factorization example
 that provided the basis for these examples.
 
 [MovieLens](http://grouplens.org/datasets/movielens/) data from [GroupLens](http://grouplens.org/).
-