The goal of this chapter is to produce rules of the following form: if a person recommends these movies, they will also recommend this movie. We will also discuss extensions where a person 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"] > 3
We 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 a training dataset. This also helps reduce the size of the dataset that will be searched...