Developing a movie recommendation module

We are now ready to build the movie recommendation engine. We will use all the functionalities that we built in the previous recipes. Let's see how it can be done.

How to do it...

1. We will create a new Python file and import the following packages:

import json 
import numpy as np 
from euclidean_score import euclidean_score 
from pearson_score import pearson_score 
from search_similar_user import search_similar_user

2. For movie recommendations for a given user, we will define a function first. We now check whether the user already exists:

# Generate recommendations for a given user 
def recommendation_generated(dataset, user): 
if user not in dataset: 
raiseTypeError('User ' + user + ' not present in the dataset') 

3. Compute the person score for the present user:

sumofall_scores= {} 
identical_sums= {} 
for u in [x for x in dataset if x != user]: 
identical_score= pearson_score(dataset, user, u) 
if identical_score<= 0: 
continue 

4. Find the movies that have...