Add Recommender System Model

To support a new RS model into the Data Integration step follow these steps:

Fork the repository, pull the latest changes and create a new branch add-model-{model_name}
Create a new folder for your model inside framework/recommender/models/.
In this new submodule, implement a Recommender subclass.
- from ...recommender import Recommender
- Your subclass must override the following methods:
  - __init__() to instantiate your class that takes as argument a config and all other arguments suited for your class.
  - name() that returns the model name. This name will be used in the experiment report as identification.
  - train() that defines the model training.
  - get_recommendations() that returns the model recommendations for all users.
  - get_user_recommendations() that return the model recommendations for a given user.
- Note: In the /recommender/utils/ you can reuse some methods that are commonly used by other models. If you are implementing a method that you believe can be reused to implement other models, please consider implementing those methods into the utils package.
Store the submodule path to dinamically load the subclass.
- Go to the recommender/model2class.py file. This file store the mapping between the model name and the submodule path and class name.
- Create a new key with the model’s name. This model name will be used to identify the RS model when using the framework.
- Store in this new key, the submodule: path, from framework/recommender/models, and the class: name.
- For example:
```
model2class = {
    # ...
    'deepwalk_based': { # model name
        'submodule': 'deep_walk_based.model',   # submodule path
        'class': 'DeepWalkBased'                # class name
    }
}
```
Add the model into the documentation
- In the file docs/source/getting_started/support.md and Models section, add the model into the list.
- Inform the model name, a reference, model summary (main components of the architecture) and parameters.
Make a Pull Request on Github.