c139.txt

Demographic Filtering- They offer generalized recommendations to every
user, based on movie popularity and/or genre.

We need a metric or score to
rate movie
● We then need to calculate
the score for every movie
● We finally need to sort the
scores and recommend the
best rated movie to the users



We first need a metric or a score to
rate the movie. Average rating, but nope.

The weighted rating formula:

((v/(v+m))*R)+((m/(v+m))*C)
Here,
● v - The number of votes for the
movies (or number of
ratings/reviews in case of an
amazon product)
● m - The minimum votes
required to be listed in the
chart
● R - Average rating of the movie
● C - Mean votes across the
whole report





If we look at our data, we already
have v(vote count) and
R(vote_average)! We can calculate
the C by calculating the mean of all
the vote averages.


Next, we want to determine an
appropriate value for m, the
minimum votes required to be listed
in the chart. Do you know how we can
do that?

We can take the 90th Percentile as
our cutoff. In other words, for a movie
to feature in the charts, it must have
more votes than at least 90% of the
rest of the movies in the list.
This is called a quantile. In future you
will be writing competitive exams
where this quantile concept will come
to place. For instance, if you get 90%
quantile then it means, you were
better than 90% of the other students
who appeared for the exam.