Learning Data Mining with Python

Recommending Movies Using Affinity Analysis
The format given here represents the full matrix, but in a more compact way.
The first row indicates that user #196 reviewed movie #242, giving it a ranking
of 3 (out of five) on the December 4, 1997.
Any combination of user and movie that isn't in this database is assumed to not exist.
This saves significant space, as opposed to storing a bunch of zeroes in memory. This
type of format is called a sparse matrix format. As a rule of thumb, if you expect
about 60 percent or more of your dataset to be empty or zero, a sparse format will
take less space to store.
When computing on sparse matrices, the focus isn't usually on the data we don't
have—comparing all of the zeroes. We usually focus on the data we have and
compare those.
The Apriori implementation
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
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