The goal of this chapter is to produce rules of the following form: if a person recommends this set of movies, they will also recommend this movie. We will also discuss extensions where a person who recommends a set of movies, is likely to recommend another particular movie.
To do this, we first need to determine if a person recommends a movie. We can do this by creating a new feature Favorable, which is True if the person gave a favorable review to a movie:
all_ratings["Favorable"] = all_ratings["Rating"] > 3We can see the new feature by viewing the dataset:
all_ratings[10:15]UserID | MovieID | Rating | Datetime | Favorable | |
10 | 62 | 257 | 2 | 1997-11-12 22:07:14 | False |
11 | 286 | 1014 | 5 | 1997-11-17 15:38:45 | True |
12 | 200 | 222 | 5 | 1997-10-05 09:05:40 | True |
13 | 210 | 40 | 3 | 1998-03-27 21:59:54 | False |
14 | 224 | 29 | 3 | 1998-02-21 23:40:57 | False |
We will sample our dataset to form training data. This also helps reduce the size of the dataset that will be searched, making the Apriori algorithm run faster. We...