This projects has explorations of handling UGC: Recommendation Engines, NLP, and more. This material is covered in Chapter 11 of Pragmatic AI
Explorations of recommendation engines
- Surprise Knn Recommendation Exploration
- Tanimoto (Or Similarity Score) Based Hand Coded Recommended Engine
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
Explorations of Cloud NLP APIS on Google, Azure and AWS
