当前位置： 首页 > news >正文

数据分析作业四-基于用户及物品数据进行内容推荐

news 2025/7/7 22:06:05 
## 导入支持库
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.metrics as metrics
import numpy as np
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import correlation
from sklearn.metrics.pairwise import pairwise_distances
import ipywidgets as widgets
from IPython.display import display, clear_output
from contextlib import contextmanager
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import os, sys
import re
import seaborn as sns

## 加载数据集并检查书籍，用户和评级数据集的形状
books = pd.read_csv('F:\\data\\bleeding_data\\BX-Books.csv',sep=None,encoding="latin-1")
books.columns = ['ISBN', 'bookTitle', 'bookAuthor','yearOfPublication', 'publisher','imageUrlS', 'imageUrlM', 'imageUrlL']users = pd.read_csv('F:\\data\\bleeding_data\\BX-Users.csv',sep=None, encoding="latin-1")
users.columns = ['userID', 'Location', 'Age']ratings = pd.read_csv('F:\\data\\bleeding_data\\BX-Book-Ratings.csv',sep=None, encoding="latin-1")
ratings.columns = ['userID', 'ISBN', 'bookRating']print (books.shape)
print (users.shape)
print (ratings.shape)

(271360, 8)
(278858, 3)
(1149780, 3)

## 一、图书数据集
books.head()
ISBNbookTitlebookAuthoryearOfPublicationpublisherimageUrlSimageUrlMimageUrlL
00195153448Classical MythologyMark P. O. Morford2002Oxford University Presshttp://images.amazon.com/images/P/0195153448.0...http://images.amazon.com/images/P/0195153448.0...http://images.amazon.com/images/P/0195153448.0...
10002005018Clara CallanRichard Bruce Wright2001HarperFlamingo Canadahttp://images.amazon.com/images/P/0002005018.0...http://images.amazon.com/images/P/0002005018.0...http://images.amazon.com/images/P/0002005018.0...
20060973129Decision in NormandyCarlo D'Este1991HarperPerennialhttp://images.amazon.com/images/P/0060973129.0...http://images.amazon.com/images/P/0060973129.0...http://images.amazon.com/images/P/0060973129.0...
30374157065Flu: The Story of the Great Influenza Pandemic...Gina Bari Kolata1999Farrar Straus Girouxhttp://images.amazon.com/images/P/0374157065.0...http://images.amazon.com/images/P/0374157065.0...http://images.amazon.com/images/P/0374157065.0...
40393045218The Mummies of UrumchiE. J. W. Barber1999W. W. Norton & Companyhttp://images.amazon.com/images/P/0393045218.0...http://images.amazon.com/images/P/0393045218.0...http://images.amazon.com/images/P/0393045218.0...
## url不需要分析，进行删除
books.drop(['imageUrlS', 'imageUrlM', 'imageUrlL'],axis=1,inplace=True)
books.head()
ISBNbookTitlebookAuthoryearOfPublicationpublisher
00195153448Classical MythologyMark P. O. Morford2002Oxford University Press
10002005018Clara CallanRichard Bruce Wright2001HarperFlamingo Canada
20060973129Decision in NormandyCarlo D'Este1991HarperPerennial
30374157065Flu: The Story of the Great Influenza Pandemic...Gina Bari Kolata1999Farrar Straus Giroux
40393045218The Mummies of UrumchiE. J. W. Barber1999W. W. Norton & Company
## books.dtypes
books.dtypes

ISBN                 object
bookTitle            object
bookAuthor           object
yearOfPublication    object
publisher            object
dtype: object

## 现在检查属性的唯一值
books.bookTitle.unique()

array(['Classical Mythology', 'Clara Callan', 'Decision in Normandy', ...,'Lily Dale : The True Story of the Town that Talks to the Dead',"Republic (World's Classics)","A Guided Tour of Rene Descartes' Meditations on First Philosophy with Complete Translations of the Meditations by Ronald Rubin"],dtype=object)

books.yearOfPublication.unique()

array(['2002', '2001', '1991', '1999', '2000', '1993', '1996', '1988','2004', '1998', '1994', '2003', '1997', '1983', '1979', '1995','1982', '1985', '1992', '1986', '1978', '1980', '1952', '1987','1990', '1981', '1989', '1984', '0', '1968', '1961', '1958','1974', '1976', '1971', '1977', '1975', '1965', '1941', '1970','1962', '1973', '1972', '1960', '1966', '1920', '1956', '1959','1953', '1951', '1942', '1963', '1964', '1969', '1954', '1950','1967', '2005', '1957', '1940', '1937', '1955', '1946', '1936','1930', '2011', '1925', '1948', '1943', '1947', '1945', '1923','2020', '1939', '1926', '1938', '2030', '1911', '1904', '1949','1932', '1928', '1929', '1927', '1931', '1914', '2050', '1934','1910', '1933', '1902', '1924', '1921', '1900', '2038', '2026','1944', '1917', '1901', '2010', '1908', '1906', '1935', '1806','2021', '2012', '2006', 'DK Publishing Inc', 'Gallimard', '1909','2008', '1378', '1919', '1922', '1897', '2024', '1376', '2037'],dtype=object)

books.loc[books.yearOfPublication == 'DK Publishing Inc',:]
books.yearOfPublication.unique()

array(['2002', '2001', '1991', '1999', '2000', '1993', '1996', '1988','2004', '1998', '1994', '2003', '1997', '1983', '1979', '1995','1982', '1985', '1992', '1986', '1978', '1980', '1952', '1987','1990', '1981', '1989', '1984', '0', '1968', '1961', '1958','1974', '1976', '1971', '1977', '1975', '1965', '1941', '1970','1962', '1973', '1972', '1960', '1966', '1920', '1956', '1959','1953', '1951', '1942', '1963', '1964', '1969', '1954', '1950','1967', '2005', '1957', '1940', '1937', '1955', '1946', '1936','1930', '2011', '1925', '1948', '1943', '1947', '1945', '1923','2020', '1939', '1926', '1938', '2030', '1911', '1904', '1949','1932', '1928', '1929', '1927', '1931', '1914', '2050', '1934','1910', '1933', '1902', '1924', '1921', '1900', '2038', '2026','1944', '1917', '1901', '2010', '1908', '1906', '1935', '1806','2021', '2012', '2006', 'DK Publishing Inc', 'Gallimard', '1909','2008', '1378', '1919', '1922', '1897', '2024', '1376', '2037'],dtype=object)

print(books.loc[books.yearOfPublication == 'DK Publishing Inc',:])

              ISBN                                          bookTitle  \
209538  078946697X  DK Readers: Creating the X-Men, How It All Beg...   
221678  0789466953  DK Readers: Creating the X-Men, How Comic Book...   bookAuthor  yearOfPublication  \
209538       2000  DK Publishing Inc   
221678       2000  DK Publishing Inc   publisher  
209538  http://images.amazon.com/images/P/078946697X.0...  
221678  http://images.amazon.com/images/P/0789466953.0...  

books.loc[books.yearOfPublication == 'DK Publishing Inc',:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
209538078946697XDK Readers: Creating the X-Men, How It All Beg...2000DK Publishing Inchttp://images.amazon.com/images/P/078946697X.0...
2216780789466953DK Readers: Creating the X-Men, How Comic Book...2000DK Publishing Inchttp://images.amazon.com/images/P/0789466953.0...
## 从上面可以看出，bookAuthor错误地装载了bookTitle，因此需要进行修正。
# ISBN '0789466953'
books.loc[books.ISBN == '0789466953','yearOfPublication'] = 2000
books.loc[books.ISBN == '0789466953','bookAuthor'] = "James Buckley"
books.loc[books.ISBN == '0789466953','publisher'] = "DK Publishing Inc"
books.loc[books.ISBN == '0789466953','bookTitle'] = "DK Readers: Creating the X-Men, How Comic Books Come to Life (Level 4: Proficient Readers)"#ISBN '078946697X'
books.loc[books.ISBN == '078946697X','yearOfPublication'] = 2000
books.loc[books.ISBN == '078946697X','bookAuthor'] = "Michael Teitelbaum"
books.loc[books.ISBN == '078946697X','publisher'] = "DK Publishing Inc"
books.loc[books.ISBN == '078946697X','bookTitle'] = "DK Readers: Creating the X-Men, How It All Began (Level 4: Proficient Readers)"

books.loc[(books.ISBN == '0789466953') | (books.ISBN == '078946697X'),:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
209538078946697XDK Readers: Creating the X-Men, How It All Beg...Michael Teitelbaum2000DK Publishing Inc
2216780789466953DK Readers: Creating the X-Men, How Comic Book...James Buckley2000DK Publishing Inc
## 继续纠正出版年鉴的类型
books.yearOfPublication=pd.to_numeric(books.yearOfPublication, errors='coerce')
sorted(books['yearOfPublication'].unique())

[0.0,1376.0,1378.0,1806.0,1897.0,1900.0,1901.0,1902.0,1904.0,1906.0,1908.0,1909.0,1910.0,1911.0,1914.0,1917.0,1919.0,1920.0,1921.0,1922.0,1923.0,1924.0,1925.0,1926.0,1927.0,1928.0,1929.0,1930.0,1931.0,1932.0,1933.0,1934.0,1935.0,1936.0,1937.0,1938.0,1939.0,1940.0,1941.0,1942.0,1943.0,1944.0,1945.0,1946.0,1947.0,1948.0,1949.0,1950.0,1951.0,1952.0,1953.0,1954.0,1955.0,1956.0,1957.0,1958.0,1959.0,1960.0,1961.0,1962.0,1963.0,1964.0,1965.0,1966.0,1967.0,1968.0,1969.0,1970.0,1971.0,1972.0,1973.0,1974.0,1975.0,1976.0,1977.0,1978.0,1979.0,1980.0,1981.0,1982.0,1983.0,1984.0,1985.0,1986.0,1987.0,1988.0,1989.0,1990.0,1991.0,1992.0,1993.0,1994.0,1995.0,1996.0,1997.0,1998.0,1999.0,2000.0,2001.0,2002.0,2003.0,2004.0,2005.0,2006.0,2008.0,2010.0,2011.0,2012.0,2020.0,2021.0,2024.0,2026.0,2030.0,2037.0,2038.0,2050.0,nan]

## 现在可以看出yearOfPublication的类型为int，其值范围为0-2050。## 由于该数据集建于2004年，我假设2006年之后的所有年份都无效，保留两年的保证金，以防数据集可能已更新。## 对于所有无效条目（包括0），我将这些条目转换为NaN，然后​​用剩余年份的平均值替换它们。
books.loc[(books.yearOfPublication > 2006) | (books.yearOfPublication == 0),'yearOfPublication'] = np.NAN

# 用年出版的平均价值代替NaNs在案例数据集被更新的情况下保留一定的空白
books.yearOfPublication.fillna(round(books.yearOfPublication.mean()), inplace=True)
books.yearOfPublication.isnull().sum()

0

books.yearOfPublication = books.yearOfPublication.astype(np.int32)

## publisher
books.loc[books.publisher.isnull(),:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
128890193169656XTyrant MoonElaine Corvidae2002NaN
1290371931696993Finders KeepersLinnea Sinclair2001NaN
## 检查行是否有书签作为查找器，看看我们是否能得到任何线索## 与不同的出版商和图书作者的所有行
books.loc[(books.bookTitle == 'Tyrant Moon'),:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
128890193169656XTyrant MoonElaine Corvidae2002NaN
books.loc[(books.bookTitle == 'Finders Keepers'),:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
10799082177364XFinders KeepersFern Michaels2002Zebra Books
420190070465037Finders KeepersBarbara Nickolae1989McGraw-Hill Companies
582640688118461Finders KeepersEmily Rodda1993Harpercollins Juvenile Books
666781575663236Finders KeepersFern Michaels1998Kensington Publishing Corporation
1290371931696993Finders KeepersLinnea Sinclair2001NaN
1343090156309505Finders KeepersWill1989Voyager Books
1734730973146907Finders KeepersSean M. Costello2002Red Tower Publications
1958850061083909Finders KeepersSharon Sala2003HarperTorch
2118740373261160Finders KeepersElizabeth Travis1993Worldwide Library
## 由图书作者检查以找到模式## 都有不同的出版商。这里没有线索
books.loc[(books.bookAuthor == 'Elaine Corvidae'),:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
1267621931696934Winter's OrphansElaine Corvidae2001Novelbooks
128890193169656XTyrant MoonElaine Corvidae2002NaN
1290010759901880WolfkinElaine Corvidae2001Hard Shell Word Factory
## 由图书作者检查以找到模式
books.loc[(books.bookAuthor == 'Linnea Sinclair'),:]
ISBNbookTitlebookAuthoryearOfPublicationpublisher
1290371931696993Finders KeepersLinnea Sinclair2001NaN
## 因为没有什么共同的东西可以推断出NaNs的发布者，将它们替换为“other”
books.loc[(books.ISBN == '193169656X'),'publisher'] = 'other'
books.loc[(books.ISBN == '1931696993'),'publisher'] = 'other'

## 二、用户数据集
print (users.shape)
users.head()

(278858, 3)
userIDLocationAge
01nyc, new york, usaNaN
12stockton, california, usa18.0
23moscow, yukon territory, russiaNaN
34porto, v.n.gaia, portugal17.0
45farnborough, hants, united kingdomNaN
users.dtypes

userID        int64
Location     object
Age         float64
dtype: object

users.userID.values

array([     1,      2,      3, ..., 278856, 278857, 278858], dtype=int64)

## Age 
sorted(users.Age.unique())

[nan,0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,30.0,31.0,32.0,33.0,34.0,35.0,36.0,37.0,38.0,39.0,40.0,41.0,42.0,43.0,44.0,45.0,46.0,47.0,48.0,49.0,50.0,51.0,52.0,53.0,54.0,55.0,56.0,57.0,58.0,59.0,60.0,61.0,62.0,63.0,64.0,65.0,66.0,67.0,68.0,69.0,70.0,71.0,72.0,73.0,74.0,75.0,76.0,77.0,78.0,79.0,80.0,81.0,82.0,83.0,84.0,85.0,86.0,87.0,88.0,89.0,90.0,91.0,92.0,93.0,94.0,95.0,96.0,97.0,98.0,99.0,100.0,101.0,102.0,103.0,104.0,105.0,106.0,107.0,108.0,109.0,110.0,111.0,113.0,114.0,115.0,116.0,118.0,119.0,123.0,124.0,127.0,128.0,132.0,133.0,136.0,137.0,138.0,140.0,141.0,143.0,146.0,147.0,148.0,151.0,152.0,156.0,157.0,159.0,162.0,168.0,172.0,175.0,183.0,186.0,189.0,199.0,200.0,201.0,204.0,207.0,208.0,209.0,210.0,212.0,219.0,220.0,223.0,226.0,228.0,229.0,230.0,231.0,237.0,239.0,244.0]

## 年龄栏有一些无效的条目，比如nan，0和非常高的值，比如100和以上
users.loc[(users.Age > 90) | (users.Age < 5), 'Age'] = np.nan

## 用平均值代替NaN
## 将数据类型设置为int
users.Age = users.Age.fillna(users.Age.mean())
users.Age = users.Age.astype(np.int32)

sorted(users.Age.unique())

[5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90]

## 三、评级数据集
ratings.shape

(1149780, 3)

## 如果每个用户对每个条目进行评级，那么评级数据集将有nusers * nbooks条目，这表明数据集非常稀疏。
n_users = users.shape[0]
n_books = books.shape[0]
print (n_users * n_books)

75670906880

ratings.head(5)
userIDISBNbookRating
0276725034545104X0
127672601550612245
227672704465208020
3276729052165615X3
427672905217950286
ratings.bookRating.unique()

array([ 0,  5,  3,  6,  8,  7, 10,  9,  4,  1,  2], dtype=int64)

ratings_new = ratings[ratings.ISBN.isin(books.ISBN)]

print (ratings.shape)
print (ratings_new.shape)

(1149780, 3)
(1031136, 3)

## 没有新用户添加，因此我们将使用高于数据集的新用户（1031136，3）
print ("number of users: " + str(n_users))
print ("number of books: " + str(n_books))

number of users: 278858
number of books: 271360

sparsity=1.0-len(ratings_new)/float(n_users*n_books)
print ('图书交叉数据集的稀疏级别是 ' +  str(sparsity*100) + ' %')

图书交叉数据集的稀疏级别是 99.99863734155898 %

ratings.bookRating.unique()

array([ 0,  5,  3,  6,  8,  7, 10,  9,  4,  1,  2], dtype=int64)

ratings_explicit = ratings_new[ratings_new.bookRating != 0]
ratings_implicit = ratings_new[ratings_new.bookRating == 0]

print (ratings_new.shape)
print( ratings_explicit.shape)
print (ratings_implicit.shape)

(1031136, 3)
(383842, 3)
(647294, 3)

## 统计
sns.countplot(data=ratings_explicit , x='bookRating')
plt.show()

在这里插入图片描述

## 基于简单流行度的推荐系统
ratings_count = pd.DataFrame(ratings_explicit.groupby(['ISBN'])['bookRating'].sum())
top10 = ratings_count.sort_values('bookRating', ascending = False).head(10)
print ("推荐下列书籍")
top10.merge(books, left_index = True, right_on = 'ISBN')

推荐下列书籍
bookRatingISBNbookTitlebookAuthoryearOfPublicationpublisher
40857870316666343The Lovely Bones: A NovelAlice Sebold2002Little, Brown
74841080385504209The Da Vinci CodeDan Brown2003Doubleday
52231340312195516The Red Tent (Bestselling Backlist)Anita Diamant1998Picador USA
21432798059035342XHarry Potter and the Sorcerer's Stone (Harry P...J. K. Rowling1999Arthur A. Levine Books
35625950142001740The Secret Life of BeesSue Monk Kidd2003Penguin Books
2625510971880107Wild AnimusRich Shapero2004Too Far
110525240060928336Divine Secrets of the Ya-Ya Sisterhood: A NovelRebecca Wells1997Perennial
70624020446672211Where the Heart Is (Oprah's Book Club (Paperba...Billie Letts1998Warner Books
23122190452282152Girl with a Pearl EarringTracy Chevalier2001Plume Books
11821790671027360Angels &amp; DemonsDan Brown2001Pocket Star
users_exp_ratings = users[users.userID.isin(ratings_explicit.userID)]
users_imp_ratings = users[users.userID.isin(ratings_implicit.userID)]

print (users.shape)
print (users_exp_ratings.shape)
print (users_imp_ratings.shape)

(278858, 3)
(68091, 3)
(52451, 3)

## 基于协同过滤的推荐系统
counts1 = ratings_explicit['userID'].value_counts()
ratings_explicit = ratings_explicit[ratings_explicit['userID'].isin(counts1[counts1 >= 100].index)]
counts = ratings_explicit['bookRating'].value_counts()
ratings_explicit = ratings_explicit[ratings_explicit['bookRating'].isin(counts[counts >= 100].index)]

ratings_matrix = ratings_explicit.pivot(index='userID', columns='ISBN', values='bookRating')
userID = ratings_matrix.index
ISBN = ratings_matrix.columns
print(ratings_matrix.shape)
ratings_matrix.head()

(449, 66574)
ISBN00009131540001046438000104687X00010472130001047973000104799X0001048082000105373600010537440001055607...B000092Q0AB00009EF82B00009NDANB0000DYXIDB0000T6KHIB0000VZEJQB0000X8HIEB00013AX9EB0001I1KOGB000234N3A
userID
2033NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2110NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2276NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4017NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4385NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

5 rows × 66574 columns

n_users = ratings_matrix.shape[0] #只考虑那些给出明确评级的用户
n_books = ratings_matrix.shape[1]
print (n_users, n_books)

449 66574

ratings_matrix.fillna(0, inplace = True)
ratings_matrix = ratings_matrix.astype(np.int32)

ratings_matrix.head(5)
ISBN00009131540001046438000104687X00010472130001047973000104799X0001048082000105373600010537440001055607...B000092Q0AB00009EF82B00009NDANB0000DYXIDB0000T6KHIB0000VZEJQB0000X8HIEB00013AX9EB0001I1KOGB000234N3A
userID
20330000000000...0000000000
21100000000000...0000000000
22760000000000...0000000000
40170000000000...0000000000
43850000000000...0000000000

5 rows × 66574 columns

sparsity=1.0-len(ratings_explicit)/float(users_exp_ratings.shape[0]*n_books)
print ('图书交叉数据集的稀疏级别是 ' +  str(sparsity*100) + ' %')

图书交叉数据集的稀疏级别是 99.99772184106935 %

## 基于用户的协同过滤
global metric,k
k=10
metric='cosine'
def findksimilarusers(user_id, ratings, metric = metric, k=k):similarities=[]indices=[]model_knn = NearestNeighbors(metric = metric, algorithm = 'brute') model_knn.fit(ratings)loc = ratings.index.get_loc(user_id)distances, indices = model_knn.kneighbors(ratings.iloc[loc, :].values.reshape(1, -1), n_neighbors = k+1)similarities = 1-distances.flatten()return similarities,indices

def predict_userbased(user_id, item_id, ratings, metric = metric, k=k):prediction=0user_loc = ratings.index.get_loc(user_id)item_loc = ratings.columns.get_loc(item_id)similarities, indices=findksimilarusers(user_id, ratings,metric, k) #similar users based on cosine similaritymean_rating = ratings.iloc[user_loc,:].mean() #to adjust for zero based indexingsum_wt = np.sum(similarities)-1product=1wtd_sum = 0 for i in range(0, len(indices.flatten())):if indices.flatten()[i] == user_loc:continue;else: ratings_diff = ratings.iloc[indices.flatten()[i],item_loc]-np.mean(ratings.iloc[indices.flatten()[i],:])product = ratings_diff * (similarities[i])wtd_sum = wtd_sum + product#在非常稀疏的数据集的情况下，使用基于协作的方法的相关度量可能会给出负面的评价#在这里的处理如下if prediction <= 0:prediction = 1   elif prediction >10:prediction = 10prediction = int(round(mean_rating + (wtd_sum/sum_wt)))print ('用户预测等级 {0} -> item {1}: {2}'.format(user_id,item_id,prediction))return prediction

## 测试
predict_userbased(11676,'0001056107',ratings_matrix)

用户预测等级 11676 -> item 0001056107: 22

## 基于项目的协同过滤
def findksimilaritems(item_id, ratings, metric=metric, k=k):similarities=[]indices=[]ratings=ratings.Tloc = ratings.index.get_loc(item_id)model_knn = NearestNeighbors(metric = metric, algorithm = 'brute')model_knn.fit(ratings)distances, indices = model_knn.kneighbors(ratings.iloc[loc, :].values.reshape(1, -1), n_neighbors = k+1)similarities = 1-distances.flatten()return similarities,indices

def predict_itembased(user_id, item_id, ratings, metric = metric, k=k):prediction= wtd_sum =0user_loc = ratings.index.get_loc(user_id)item_loc = ratings.columns.get_loc(item_id)similarities, indices=findksimilaritems(item_id, ratings) #similar users based on correlation coefficientssum_wt = np.sum(similarities)-1product=1for i in range(0, len(indices.flatten())):if indices.flatten()[i] == item_loc:continue;else:product = ratings.iloc[user_loc,indices.flatten()[i]] * (similarities[i])wtd_sum = wtd_sum + product                              prediction = int(round(wtd_sum/sum_wt))#在非常稀疏的数据集的情况下，使用基于协作的方法的相关度量可能会给出负面的评价#在这里处理的是下面的//代码，没有下面的代码片段，下面的代码片段是为了避免负面影响#在使用相关度规时，可能会出现非常稀疏的数据集的预测if prediction <= 0:prediction = 1   elif prediction >10:prediction = 10print ('用户预测等级 {0} -> item {1}: {2}'.format(user_id,item_id,prediction)    )  return prediction

## 测试
prediction = predict_itembased(11676,'0001056107',ratings_matrix)

用户预测等级 11676 -> item 0001056107: 1

相关文章：

数据分析作业四-基于用户及物品数据进行内容推荐

## 导入支持库 import pandas as pd import matplotlib.pyplot as plt import sklearn.metrics as metrics import numpy as np from sklearn.neighbors import NearestNeighbors from scipy.spatial.distance import correlation from sklearn.metrics.pairwise import pairwi…...

编程日记 2023/8/29 8:11:37

在腾讯云服务器OpenCLoudOS系统中安装svn（有图详解)

1. 安装svn yum -y install subversion 安装成功&#xff1a; 2. 创建数据根目录及仓库 mkdir -p /usr/local/svn/svnrepository 创建test仓库&#xff1a; svnadmin create /usr/local/svn/test test仓库创建成功&#xff1a; 3. 修改配置test仓库 cd /usr/local/svn/te…...

编程日记 2023/8/29 8:10:36

C语言日常刷题5

文章目录 题目答案与解析1234567、 题目 1、以下叙述中正确的是&#xff08; &#xff09; A: 只能在循环体内和switch语句体内使用break语句 B: 当break出现在循环体中的switch语句体内时&#xff0c;其作用是跳出该switch语句体&#xff0c;并中止循环体的执行 C: continue语…...

编程日记 2023/8/29 8:09:34

【LeetCode-中等题】73. 矩阵置零

题目 题解一&#xff1a;使用标记数组 public void setZeroes(int[][] matrix) {int m matrix.length;int n matrix[0].length;boolean[] row new boolean[m];boolean[] col new boolean[n];for(int i0; i< m;i){for(int j 0;j<n;j){if (matrix[i][j] 0) row[i]col…...

编程日记 2023/8/29 8:08:33

本地部署 FastGPT

本地部署 FastGPT 1. FastGPT 是什么2. 部署 FastGPT 1. FastGPT 是什么 FastGPT 是一个基于 LLM 大语言模型的知识库问答系统&#xff0c;提供开箱即用的数据处理、模型调用等能力。同时可以通过 Flow 可视化进行工作流编排&#xff0c;从而实现复杂的问答场景&#xff01; …...

编程日记 2023/8/29 8:07:31

软件工程(十八) 行为型设计模式(四)

1、状态模式 简要说明 允许一个对象在其内部改变时改变它的行为 速记关键字 状态变成类 类图如下 状态模式主要用来解决对象在多种状态转换时，需要对外输出不同的行为的问题。比如订单从待付款到待收货的咋黄台发生变化，执行的逻辑是不一样的。 所以我们将状态抽象为一…...

编程日记 2023/8/29 8:06:29

Socket通信与WebSocket协议

文章目录 目录 文章目录 前言 一、Socket通信 1.1 BIO 1.2 NIO 1.3 AIO 二、WebSocket协议 总结 前言 一、Socket通信 Socket是一种用于网络通信的编程接口&#xff08;API&#xff09;&#xff0c;它提供了一种机制&#xff0c;使不同主机之间可以通过网络进行数据传输和通信…...

编程日记 2023/8/29 8:05:27

新KG视点 | Jeff Pan、陈矫彦等——大语言模型与知识图谱的机遇与挑战

OpenKG 大模型专辑 导读 知识图谱和大型语言模型都是用来表示和处理知识的手段。大模型补足了理解语言的能力&#xff0c;知识图谱则丰富了表示知识的方式&#xff0c;两者的深度结合必将为人工智能提供更为全面、可靠、可控的知识处理方法。在这一背景下&#xff0c;OpenKG组织…...

编程日记 2023/8/29 8:04:26

详解过滤器Filter和拦截器Interceptor的区别和联系

目录 前言 区别 联系 前言 过滤器(Filter)和拦截器(Interceptor)都是用于在Web应用程序中处理请求和响应的组件&#xff0c;但它们在实现方式和功能上有一些区别。 区别 1. 实现方式&#xff1a; - 过滤器是基于Servlet规范的组件&#xff0c;通过实现javax.servlet.Filt…...

编程日记 2023/8/29 8:03:23

List常用的操作

1、看List里是否存在某个元素 contains //省略建立listboolean contains stringList.contains("上海");System.out.println(contains); 如果存在是true&#xff0c;不存在是false 2、看某个元素在List中的索引号 .indexOf List<String>stringList new Ar…...

编程日记 2023/8/29 8:02:22

Android studio APK切换多个摄像头(Camera2)

1.先设置camera的权限 <uses-permission android:name"android.permission.CAMERA" /> 2.布局 <?xml version"1.0" encoding"utf-8"?> <LinearLayout xmlns:android"http://schemas.android.com/apk/res/android"and…...

编程日记 2023/8/29 8:01:20

ChatGPT 对教育的影响，AI 如何颠覆传统教育

胜量老师 来源&#xff1a;BV1Nv4y1H7kC 由Chat GPT引发的对教育的思考&#xff0c;人类文明发展至今一直靠教育完成文明的传承&#xff0c;一个年轻人要经历若干年的学习&#xff0c;才能进入社会投入对文明的建设&#xff0c;而学习中有大量内容是需要记忆和反复训练的。 无…...

编程日记 2023/8/29 8:00:18

Spring(九）声明式事务

Spring整合Junit4和JdbcTemplater如下所示&#xff1a; 我们所使用的junit的jar包不同&#xff0c;可以整合不同版本的junit。 我们导入的依赖如下所示&#xff1a; <?xml version"1.0" encoding"UTF-8"?> <project xmlns"http://maven.a…...

编程日记 2023/8/29 7:59:17

java中用HSSFWorkbook生成xls格式的excel(亲测)

SXSSFWorkbook类是用于生成XLSX格式的Excel文件&#xff08;基于XML格式&#xff09;&#xff0c;而不是XLS格式的Excel文件&#xff08;基于二进制格式&#xff09;。 如果你需要生成XLS格式的Excel文件&#xff0c;可以使用HSSFWorkbook类。以下是一个简单的示例&#xff1a…...

编程日记 2023/8/29 7:58:16

做平面设计一般电脑可以吗 优漫动游

平面设计常用的软件如下&#xff1a;Photoshop、AutoCAD、AI等。其中对电脑配置要求高的是AutoCAD&#xff0c;可运行AutoCAD的软件均可运行如上软件。 做平面设计一般电脑可以吗 AutoCAD64位版配置要求&#xff1a;AMDAthlon64位处理器、支持SSE2技术的AMDOpteron处理器、…...

编程日记 2023/8/29 7:57:15

设计模式备忘录+命令模式实现Word撤销恢复操作

文章目录 前言思路代码实现uml类图总结 前言 最近学习设计模式行为型的模式&#xff0c;学到了备忘录模式提到这个模式可以记录一个对象的状态属性值&#xff0c;用于下次复用&#xff0c;于是便想到了我们在Windows系统上使用的撤销操作&#xff0c;于是便想着使用这个模式进…...

编程日记 2023/8/29 7:56:13

Linux centos7 bash编程小训练

训练要求&#xff1a; 求比一个数小的最大回文数 知识点&#xff1a; 一个数字正读反读都一样&#xff0c;我们称为回文数&#xff0c;如5、11、55、121、222等。 我们训练用bash编写一个小程序&#xff0c;由我们标准输入一个整数&#xff0c;计算机将显示出一个比这个数小…...

编程日记 2023/8/29 7:55:11

创作2周年纪念日-特别篇

创作2周年纪念日-特别篇 1. 与CSDN的机缘2. 收获3. 憧憬 1. 与CSDN的机缘 很荣幸&#xff0c;在大学时候&#xff0c;能够接触到CSDN这样一个平台&#xff0c;当时对嵌入式开发、编程、计算机视觉等内容比较感兴趣。后面一个很偶然的联培实习机会&#xff0c;让我接触到了Pych…...

编程日记 2023/8/29 7:54:10

【UE5】用法简介-使用MAWI高精度树林资产的地形材质与添加风雪效果

首先我们新建一个basic工程 然后点击floor按del键&#xff0c;把floor给删除。 只留下空白场景 点击“地形” 在这个范例里&#xff0c;我只创建一个500X500大小的地形&#xff0c;只为了告诉大家用法&#xff0c;点击创建 创建好之后有一大片空白的地形出现 让我们点左上角…...

编程日记 2023/8/29 7:53:09

兼容AD210 车规级高精度隔离放大器：ISO EM210

车规级高精度隔离放大器&#xff1a;ISO EM210 Pin-Pin兼容AD210的低成本,小体积DIP标准38Pin金属外壳封装模块&#xff0c;能有效屏蔽现场EMC空间干扰。功能设计全面&#xff0c;采用非固定增益方式&#xff0c;输入信号经过输入端的前置放大器&#xff08;增益为1-100&#x…...

编程日记 2023/8/29 7:52:07

网络编程（Modbus进阶）

思维导图 Modbus RTU&#xff08;先学一点理论&#xff09; 概念 Modbus RTU 是工业自动化领域 最广泛应用的串行通信协议&#xff0c;由 Modicon 公司&#xff08;现施耐德电气&#xff09;于 1979 年推出。它以 高效率、强健性、易实现的特点成为工业控制系统的通信标准。 包…...

编程新知 2025/7/6 13:54:14

基于算法竞赛的c++编程（28）结构体的进阶应用

结构体的嵌套与复杂数据组织 在C中&#xff0c;结构体可以嵌套使用&#xff0c;形成更复杂的数据结构。例如&#xff0c;可以通过嵌套结构体描述多层级数据关系&#xff1a; struct Address {string city;string street;int zipCode; };struct Employee {string name;int id;…...

编程新知 2025/6/17 5:30:27

测试微信模版消息推送

进入“开发接口管理”--“公众平台测试账号”&#xff0c;无需申请公众账号、可在测试账号中体验并测试微信公众平台所有高级接口。 获取access_token: 自定义模版消息&#xff1a; 关注测试号&#xff1a;扫二维码关注测试号。 发送模版消息&#xff1a; import requests da…...

编程新知 2025/7/5 17:14:23

边缘计算医疗风险自查APP开发方案

核心目标：在便携设备（智能手表/家用检测仪）部署轻量化疾病预测模型，实现低延迟、隐私安全的实时健康风险评估。 一、技术架构设计 #mermaid-svg-iuNaeeLK2YoFKfao {font-family:"trebuchet ms",verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg…...

编程新知 2025/7/6 21:25:50

uni-app学习笔记二十二---使用vite.config.js全局导入常用依赖

在前面的练习中&#xff0c;每个页面需要使用ref&#xff0c;onShow等生命周期钩子函数时都需要像下面这样导入 import {onMounted, ref} from "vue" 如果不想每个页面都导入&#xff0c;需要使用node.js命令npm安装unplugin-auto-import npm install unplugin-au…...

编程新知 2025/6/19 5:54:07

Robots.txt 文件

什么是robots.txt&#xff1f; robots.txt 是一个位于网站根目录下的文本文件&#xff08;如&#xff1a;https://example.com/robots.txt&#xff09;&#xff0c;它用于指导网络爬虫&#xff08;如搜索引擎的蜘蛛程序&#xff09;如何抓取该网站的内容。这个文件遵循 Robots…...

编程新知 2025/7/7 19:55:16

实现弹窗随键盘上移居中

实现弹窗随键盘上移的核心思路 在Android中&#xff0c;可以通过监听键盘的显示和隐藏事件&#xff0c;动态调整弹窗的位置。关键点在于获取键盘高度&#xff0c;并计算剩余屏幕空间以重新定位弹窗。 // 在Activity或Fragment中设置键盘监听 val rootView findViewById<V…...

编程新知 2025/6/15 9:35:48

华硕a豆14 Air香氛版，美学与科技的馨香融合

在快节奏的现代生活中&#xff0c;我们渴望一个能激发创想、愉悦感官的工作与生活伙伴&#xff0c;它不仅是冰冷的科技工具&#xff0c;更能触动我们内心深处的细腻情感。正是在这样的期许下&#xff0c;华硕a豆14 Air香氛版翩然而至&#xff0c;它以一种前所未有的方式&#x…...

编程新知 2025/7/7 8:53:20

CSS设置元素的宽度根据其内容自动调整

width: fit-content 是 CSS 中的一个属性值&#xff0c;用于设置元素的宽度根据其内容自动调整&#xff0c;确保宽度刚好容纳内容而不会超出。 效果对比 默认情况&#xff08;width: auto&#xff09;&#xff1a; 块级元素&#xff08;如 <div>&#xff09;会占满父容器…...

编程新知 2025/6/20 15:09:15

招商蛇口 | 执笔CID，启幕低密生活新境

作为中国城市生长的力量&#xff0c;招商蛇口以“美好生活承载者”为使命&#xff0c;深耕全球111座城市&#xff0c;以央企担当匠造时代理想人居。从深圳湾的开拓基因到西安高新CID的战略落子&#xff0c;招商蛇口始终与城市发展同频共振&#xff0c;以建筑诠释对土地与生活的…...

编程新知 2025/7/7 4:58:29