基于内容的推荐例子（电影推荐）

  • Z时代
  • 2024-01-10
  • 分类：综合

coding

推荐例子介绍

根据典型关键数据

导演

演员

关键字

题材

'keywords', 'cast', 'genres', 'director'

构造自然语言的组合特征，利用CountVectorizer计算每个词出现的次数，作为特征向量，

使用余弦相似性构造所有电影之间的相似性。

代码

https://github.com/fanqingsong/Content-based-Recommandation-Engine

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
def get_title_from_index(index):
return df[df.index == index]["title"].values[0]
def get_index_from_title(title):
return df[df.title == title]["index"].values[0]
# Reading CSV File
df = pd.read_csv("movie_dataset_content.csv", encoding='utf-8')
# Selecting Features
features = ['keywords', 'cast', 'genres', 'director']
# Creating a column in DF which combines all selected features
for feature in features:
    df[feature] = df[feature].fillna('')
def combine_features(row):
return row['keywords'] + "" + row['cast'] + "" + row["genres"] + "" + row["director"]
df["combined_features"] = df.apply(combine_features, axis=1)
# making an object of CountVectorizer class to create count matrix
cv = CountVectorizer()
# Creating count matrix from this new combined column
count_matrix = cv.fit_transform(df["combined_features"])
# Computing the Cosine Similarity based on the count_matrix
cosine_sim = cosine_similarity(count_matrix)
movie_liked_by_user = "Thor"
# Getting index of this movie from its title
liked_movie_index = get_index_from_title(movie_liked_by_user)
similar_movies = list(enumerate(cosine_sim[liked_movie_index]))
# Get a list of similar movies in descending order of similarity score
predictions = sorted(similar_movies, key=lambda x: x[1], reverse=True)
# Print titles of 10 predicted movies
i = 0
for movie in predictions:
print(get_title_from_index(movie[0]))
    i = i+1
if i > 10:
break

运行

root@DESKTOP-OGSLB14:~/mine/Content-based-Recommandation-Engine# python3 content_based_recommender.py
Thor
Thor: The Dark World
The Avengers
Captain America: The Winter Soldier
Avengers: Age of Ultron
Captain America: Civil War
Pirates of the Caribbean: Dead Man's Chest
Cinderella
Jack Ryan: Shadow Recruit
The Amazing Spider-Man 2
Captain America: The First Avenger
root@DESKTOP-OGSLB14:~/mine/Content-based-Recommandation-Engine#

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