根据一本书的点击量进行个性化推荐算法的代码
时间: 2024-05-03 10:17:59 浏览: 10
以下是基于书籍点击量的个性化推荐算法的代码,其中使用了协同过滤算法:
```python
import numpy as np
# 创建一个字典,键为用户ID,值为一个列表,包含该用户点击过的书籍ID
user_books = {
"user1": ["book1", "book3", "book5"],
"user2": ["book2", "book4"],
"user3": ["book1", "book2", "book3"],
"user4": ["book4", "book5"],
"user5": ["book2", "book3", "book5"]
}
# 创建一个字典,键为书籍ID,值为一个列表,包含所有点击过该书籍的用户ID
book_users = {
"book1": ["user1", "user3"],
"book2": ["user2", "user3", "user5"],
"book3": ["user1", "user3", "user5"],
"book4": ["user2", "user4"],
"book5": ["user1", "user4", "user5"]
}
# 创建一个矩阵,表示每个用户对每本书籍的评分,初始值为0
ratings = np.zeros((len(user_books), len(book_users)))
# 将评分矩阵中的值设置为用户对书籍的点击次数
for i, user in enumerate(user_books):
for j, book in enumerate(book_users):
if user in book_users[book]:
ratings[i, j] = len(book_users[book])
# 计算用户之间的相似度,使用余弦相似度公式
def cosine_similarity(u, v):
return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))
# 计算用户之间的相似度矩阵
similarity_matrix = np.zeros((len(user_books), len(user_books)))
for i in range(len(user_books)):
for j in range(i+1, len(user_books)):
similarity_matrix[i, j] = cosine_similarity(ratings[i], ratings[j])
similarity_matrix[j, i] = similarity_matrix[i, j]
# 获取与指定用户最相似的K个用户
def get_top_k_similar_users(user_id, k):
user_index = list(user_books.keys()).index(user_id)
similar_users = np.argsort(similarity_matrix[user_index])[::-1][1:k+1]
return [list(user_books.keys())[i] for i in similar_users]
# 获取相似用户点击过的书籍
def get_similar_books(similar_users):
similar_books = []
for user in similar_users:
similar_books.extend(user_books[user])
return list(set(similar_books))
# 获取推荐书籍
def get_recommendations(user_id, k):
similar_users = get_top_k_similar_users(user_id, k)
similar_books = get_similar_books(similar_users)
user_books_set = set(user_books[user_id])
recommendations = [book for book in similar_books if book not in user_books_set]
return recommendations
# 测试推荐算法
user_id = "user1"
k = 2
recommendations = get_recommendations(user_id, k)
print("为用户{}推荐的书籍有:{}".format(user_id, recommendations))
```
这段代码可以根据输入的`user_id`和`k`,推荐给该用户最感兴趣的$k$本书籍。其中,我们使用余弦相似度计算用户之间的相似度,找出最相似的$k$个用户,然后根据这些用户点击过的书籍,推荐给该用户未点击过的书籍。
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