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README.md

1<!--- Licensed to the Apache Software Foundation (ASF) under one -->
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17
18# Recommender Systems
19
20
21This directory has a set of examples of how to build various kinds of recommender systems
22using MXNet. The sparsity of user / item data is handled through the embedding layers that accept
23indices as input rather than one-hot encoded vectors.
24
25
26## Examples
27
28The examples are driven by notebook files.
29
30* [Matrix Factorization: linear and non-linear models](demo1-MF.ipynb)
31* [Deep Structured Semantic Model (DSSM) for content-based recommendations](demo2-dssm.ipynb)
32
33
34### Negative Sampling
35
36* A previous version of this example had an example of negative sampling. For example of negative sampling, please refer to:
37    [Gluon NLP Sampled Block](https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/model/sampled_block.py)
38
39
40## Acknowledgements
41
42Thanks to [xlvector](https://github.com/xlvector/) for the first Matrix Factorization example
43that provided the basis for these examples.
44
45[MovieLens](http://grouplens.org/datasets/movielens/) data from [GroupLens](http://grouplens.org/).
46Note: MovieLens 100K and 10M dataset are copy right to GroupLens Research Group at the University of Minnesota,
47and licensed under their usage license. For full text of the usage license, see [ml-100k license](http://files.grouplens.org/datasets/movielens/ml-100k-README.txt)
48 and [ml-10m license](http://files.grouplens.org/datasets/movielens/ml-10m-README.html).