The rec module

[jrzaurin]

• After a number of issues opened and questions in slack about recommendation algorithms in the library I decided to include a rec module that initially contains a small number of recommendation algorithms.

These are

1. Factorisation Machines (FM) and DeepFM
2. Field Aware Factorisation Machines (FFM) and DeepFFM
3. Extreme Deep Factorisation Machines (xDeepFM)
4. Deep Interest Networks (DIN)

We will add more in the near future.

• In addition some bugs were fixed (Multitargets regression "None"prediction issue #232 and Multitargets regression "val_split" issue #233)

[codecov]

Codecov Report

Attention: Patch coverage is 97.59615% with 10 lines in your changes missing coverage. Please review.

Project coverage is 94.80%. Comparing base (220eb3f) to head (8dd61ce).
Report is 34 commits behind head on master.

Files with missing lines	Patch %	Lines
pytorch_widedeep/models/rec/din.py	98.41%	2 Missing ⚠️
...torch_widedeep/models/tabular/embeddings_layers.py	33.33%	2 Missing ⚠️
pytorch_widedeep/training/trainer.py	77.77%	2 Missing ⚠️
pytorch_widedeep/models/_get_activation_fn.py	50.00%	1 Missing ⚠️
pytorch_widedeep/models/rec/deepfm.py	97.56%	1 Missing ⚠️
...rch_widedeep/models/tabular/_base_tabular_model.py	90.90%	1 Missing ⚠️
...eep/models/tabular/transformers/tab_transformer.py	90.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #234      +/-   ##
==========================================
+ Coverage   94.67%   94.80%   +0.12%     
==========================================
  Files         116      121       +5     
  Lines        7252     7579     +327     
==========================================
+ Hits         6866     7185     +319     
- Misses        386      394       +8     

Flag	Coverage Δ
unittests	94.80% <97.59%> (?)

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