ML之RS：基于用户的CF+LFM实现的推荐系统(基于相关度较高的用户实现电影推荐)

ML之RS：基于用户的CF+LFM实现的推荐系统(基于相关度较高的用户实现电影推荐)

目录

输出结果

实现代码

输出结果

实现代码

  

   

    

     

    
    

     

      #ML之RS：基于CF和LFM实现的推荐系统
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import pandas as pd
     
    
   

    

     

    
    

     

      import matplotlib.pyplot as plt
     
    
   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     

      import warnings
     
    
   

    

     

    
    

     

      warnings.filterwarnings('ignore')
     
    
   

    

     

    
    

     

      np.random.seed(1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      plt.style.use('ggplot')
     
    
   

    

     

    
    

     

      # data = pd.read_csv('ml-20m/ratings_smaller.csv', index_col=0)
     
    
   

    

     

    
    

     

      # movies = pd.read_csv('ml-20m/movies_smaller.csv')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #1、导入数据集
     
    
   

    

     

    
    

     

      data = pd.read_csv('ml-latest-small/ratings.csv')
     
    
   

    

     

    
    

     

      movies = pd.read_csv('ml-latest-small/movies.csv')
     
    
   

    

     

    
    

     

      movies = movies.set_index('movieId')[['title', 'genres']]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #2、观察数据集
     
    
   

    

     

    
    

     

      # How many users?
     
    
   

    

     

    
    

     

      print (data.userId.nunique(), 'users')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # How many movies?
     
    
   

    

     

    
    

     

      print (data.movieId.nunique(), 'movies')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # How possible ratings?
     
    
   

    

     

    
    

     

      print (data.userId.nunique() * data.movieId.nunique(), 'possible ratings')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # How many do we have?
     
    
   

    

     

    
    

     

      print (len(data), 'ratings')
     
    
   

    

     

    
    

     

      print (100 * (float(len(data)) / (data.userId.nunique() * data.movieId.nunique())), '% of possible ratings')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Number of ratings per users
     
    
   

    

     

    
    

     

      fig = plt.figure(figsize=(10, 10))
     
    
   

    

     

    
    

     

      ax = plt.hist(data.groupby('userId').apply(lambda x: len(x)).values, bins=50)
     
    
   

    

     

    
    

     

      plt.xlabel("ratings")
     
    
   

    

     

    
    

     

      plt.ylabel("users")
     
    
   

    

     

    
    

     

      plt.title("Number of ratings per user")
     
    
   

    

     

    
    

     

      plt.show()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Number of ratings per movie
     
    
   

    

     

    
    

     

      fig = plt.figure(figsize=(10, 10))
     
    
   

    

     

    
    

     

      ax = plt.hist(data.groupby('movieId').apply(lambda x: len(x)).values, bins=50)
     
    
   

    

     

    
    

     

      plt.xlabel("ratings")
     
    
   

    

     

    
    

     

      plt.ylabel("movies")
     
    
   

    

     

    
    

     

      plt.title('Number of ratings per movie')
     
    
   

    

     

    
    

     

      plt.show()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Ratings distribution评分分布
     
    
   

    

     

    
    

     

      fig = plt.figure(figsize=(10, 10))
     
    
   

    

     

    
    

     

      ax = plt.hist(data.rating.values, bins=5)
     
    
   

    

     

    
    

     

      plt.xlabel("ratings")
     
    
   

    

     

    
    

     

      plt.ylabel("numbers")
     
    
   

    

     

    
    

     

      plt.title("Distribution of ratings")
     
    
   

    

     

    
    

     

      plt.show()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Average rating per user
     
    
   

    

     

    
    

     

      fig = plt.figure(figsize=(10, 10))
     
    
   

    

     

    
    

     

      ax = plt.hist(data.groupby('userId').rating.mean().values, bins=10)
     
    
   

    

     

    
    

     

      plt.xlabel("Average rating")
     
    
   

    

     

    
    

     

      plt.ylabel("numbers")
     
    
   

    

     

    
    

     

      plt.title("Average rating per user")
     
    
   

    

     

    
    

     

      plt.show()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Average rating per movie
     
    
   

    

     

    
    

     

      fig = plt.figure(figsize=(10, 10))
     
    
   

    

     

    
    

     

      ax = plt.hist(data.groupby('movieId').rating.mean().values, bins=10)
     
    
   

    

     

    
    

     

      plt.title('Average rating per movie')
     
    
   

    

     

    
    

     

      plt.show()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Top Movies，genres电影类型
     
    
   

    

     

    
    

     

      average_movie_rating = data.groupby('movieId').mean()
     
    
   

    

     

    
    

     

      top_movies = average_movie_rating.sort_values('rating', ascending=False).head(10)
     
    
   

    

     

    
    

     

      pd.concat([movies.loc[top_movies.index.values],
     
    
   

    

     

    
    

     

       average_movie_rating.loc[top_movies.index.values].rating], axis=1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Robust Top Movies - Lets weight the average rating by the square root of number of ratings让平均评分进行加权数的平方根
     
    
   

    

     

    
    

     

      top_movies = data.groupby('movieId').apply(lambda x:len(x)**0.5 * x.mean()).sort_values('rating', ascending=False).head(10)
     
    
   

    

     

    
    

     

      pd.concat([movies.loc[top_movies.index.values], 
     
    
   

    

     

    
    

     

       average_movie_rating.loc[top_movies.index.values].rating], axis=1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      controversial_movies = data.groupby('movieId').apply(lambda x:len(x)**0.25 * x.std()).sort_values('rating', ascending=False).head(10)
     
    
   

    

     

    
    

     

      pd.concat([movies.loc[controversial_movies.index.values], 
     
    
   

    

     

    
    

     

       average_movie_rating.loc[controversial_movies.index.values].rating], axis=1)
     
    
  
 

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原文链接：yunyaniu.blog.csdn.net/article/details/81835446