AI on User Generated Content

This projects has explorations of handling UGC: Recommendation Engines, NLP, and more. This material is covered in Chapter 11 of Pragmatic AI

Jupyter Notebooks for UGC (User Generated Content)

Recommendations

Explorations of recommendation engines

How to use:

In [1]: follows import *
In [2]: df = follows_dataframe()
In [3]: dfr = follow_relations_df(df)
In [4]: dfr.head()
In [5]: scores = generate_similarity_scores(dfr, "00480160-0e6a-11e6-b5a1-06f8ea4c790f")
In [5]: scores
Out[5]: 
2144    0.000000
713     0.000000
714     0.000000
715     0.000000
716     0.000000
717     0.000000
712     0.000000
980     0.333333
2057    0.333333
3       1.000000
Name: follow_relations, dtype: float64
In [6]: dfs = return_similarity_scores_with_ids(dfr, scores)
In [6]: dfs
Out[6]: 
                                followerId  \
980   76cce300-0e6a-11e6-83e2-0242528e2f1b   
2057  f5ccbf50-0e69-11e6-b5a1-06f8ea4c790f   
3     00480160-0e6a-11e6-b5a1-06f8ea4c790f   
                                       follow_relations    scores  \
980   [f5ccbf50-0e69-11e6-b5a1-06f8ea4c790f, 0048016...  0.333333   
2057  [76cce300-0e6a-11e6-83e2-0242528e2f1b, 0048016...  0.333333   
3     [f5ccbf50-0e69-11e6-b5a1-06f8ea4c790f, 76cce30...         1   
      following_count  
980                 2  
2057                2  
3                   2

Cloud NLP

Explorations of Cloud NLP APIS on Google, Azure and AWS

Kernel Density Plot of NLP Azure Call