Overview of binary, implicit ratings

Créer des moteurs de recommandation avec PySpark

Jamen Long

Data Scientist at Nike

Binary ratings

binary_movie_ratings.show()

+------+-------+-------------+
|userId|movieId|binary_rating|
+------+-------+-------------+
|    26|    474|            0|
|    26|   2529|            1|
|    26|     26|            0|
|    26|   1950|            0|
|    26|   4823|            1|
|    26|  72011|            1|
|    26| 142507|            0|
|    38|   1325|            0|
|    38|   6011|            1|
+------+-------+-------------+

Class imbalance

getSparsity(binary_ratings)

Sparsity: .993

Item weighting

Item Weighting: Movies with more user views = higher weight

Item weighting and user weighting

Item Weighting: Movies with more user views = higher weight
User Weighting: Users that have seen more movies will have lower weights applied to unseen movies

Let's practice!

Créer des moteurs de recommandation avec PySpark