I need to make a movie recommendation from text using spacy - python-3.x

Question

Thank you for taking the time to read this
I AM NOT ASKING TO DO MY HOMEWORK FOR ME... Just need guidance
I have a homework problem that I can't figure out. I need to do the following with the spacy library in python.
The Homework Question
Read in the movies.txt file. Each separate line is a description of a different movie.
Your task is to create a function to return which movies a user would watch
next if they have watched Planet Hulk with the description “Will he save
their world or destroy it? When the Hulk becomes too dangerous for the
Earth, the Illuminati trick Hulk into a shuttle and launch him into space to a planet where the Hulk can live in peace. Unfortunately, Hulk land on the planet Sakaar where he is sold into slavery and trained as a gladiator.”
The function should take in the description as a parameter and return the
title of the most similar movie.
The movie.txt file contains the following:
Movie A :When Hiccup discovers Toothless isn't the only Night Fury, he must seek "The Hidden World", a secret Dragon Utopia before a hired tyrant named Grimmel finds it first.
Movie B :After the death of Superman, several new people present themselves as possible successors.
Movie C :A darkness swirls at the center of a world-renowned dance company, one that will engulf the artistic director, an ambitious young dancer, and a grieving psychotherapist. Some will succumb to the nightmare. Others will finally wake up.
Movie D :A humorous take on Sir Arthur Conan Doyle's classic mysteries featuring Sherlock Holmes and Doctor Watson.
Movie E :A 16-year-old girl and her extended family are left reeling after her calculating grandmother unveils an array of secrets on her deathbed.
Movie F :In the last moments of World War II, a young German soldier fighting for survival finds a Nazi captain's uniform. Impersonating an officer, the man quickly takes on the monstrous identity of the perpetrators he is trying to escape from.
Movie G :The world at an end, a dying mother sends her young son on a quest to find the place that grants wishes.
Movie H :A musician helps a young singer and actress find fame, even as age and alcoholism send his own career into a downward spiral.
Movie I :Corporate analyst and single mom, Jen, tackles Christmas with a business-like approach until her uncle arrives with a handsome stranger in tow.
Movie J :Adapted from the bestselling novel by Madeleine St John, Ladies in Black is an alluring and tender-hearted comedy drama about the lives of a group of department store employees in 1959 Sydney.
Things that I have tried:
I have tried looking for for a feature in spacy that does something like this but the only thing I can come across is the similarity function but that only checks if the sentence has similar values...
Yes I am new to Spacy
My code so far
from __future__ import unicode_literals
import spacy
nlp = spacy.load("en_core_web_md")
myfile = open("movies.txt").read()
NlpRead = nlp(myfile)
sentence_to_compare = "Will he save their world or destroy it? When the Hulk becomes too dangerous for the Earth, the Illuminati trick Hulk into a shuttle and launch him into space to a planet where the Hulk can live in peace. Unfortunately, Hulk land on the planet Sakaar where he is sold into slavery and trained as a gladiator"
model_sentences = nlp(sentence_to_compare)
for sentence in myfile:
similarity = nlp(sentence).similarity(model_sentences)
print(sentence + "-" + str(similarity))

Answer 1

Spacy has several available pre-trained models. You are using "en_core_web_md" which includes word vectors. According to the documentation these included word vectors are 'GloVe vectors trained on Common Crawl'.
As shown in the code and heatmap below, these word vectors capture semantic similarity, and can help you cluster topics.
Naturally, this is not a solution to your homework problem, but a hint about a technique which you may find useful.
import spacy
nlp = spacy.load("en_core_web_md")
tokens = nlp(u'Hulk Superman Batman dragon elf dance musical handsome romance war soldier')
for token in tokens:
print(token.text, token.has_vector, token.vector_norm, token.is_oov)
labels = [a.text for a in tokens]
print(labels)
M = np.zeros((len(tokens), len(tokens)))
for idx, token1 in enumerate(tokens):
for idy, token2 in enumerate(tokens):
M[idx, idy] = token1.similarity(token2)
%matplotlib inline
import numpy as np
import seaborn as sns
import matplotlib.pylab as plt
ax = sns.heatmap(M, cmap = "RdBu_r", xticklabels=labels, yticklabels=labels)
plt.show()
Also, Spacy also provides Part-of-speech tagging with which you can extract proper nouns and common nouns from sentences:
doc = nlp("Will he save their world or destroy it? When the Hulk becomes too dangerous for the Earth, the Illuminati trick Hulk into a shuttle and launch him into space to a planet where the Hulk can live in peace. Unfortunately, Hulk land on the planet Sakaar where he is sold into slavery and trained as a gladiator")
properNouns = [token.text for token in doc if token.pos_ =='PROPN']
commonNouns = [token.text for token in doc if token.pos_ =='NOUN']
print(properNouns)
# ['Hulk', 'Earth', 'Illuminati', 'Hulk', 'Hulk', 'Hulk', 'Sakaar']
print(commonNouns)
# ['world', 'shuttle', 'space', 'planet', 'peace', 'land', 'planet', 'slavery', 'gladiator']