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)
  
 

相关文章推荐

GitHub

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

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