Data bias problem in intelligent recommendation system

The problem of data deviation in intelligent recommendation systems requires specific code examples

With the rapid development of intelligent technology, intelligent recommendation systems play a role in our daily lives increasingly important role. Whether we are shopping on e-commerce platforms or looking for recommendations in entertainment fields such as music and movies, we can all feel the direct impact of intelligent recommendation systems. However, as the amount of data increases, the problem of data bias in intelligent recommendation systems gradually becomes apparent.

Data bias problem refers to the inaccuracy of recommendation results due to the uneven distribution of sample data or the existence of personalized preferences. Specifically, the number of some samples far exceeds that of other samples, causing the system to encounter "hot recommendations" or "long tail problems" when making recommendations, that is, only popular products or certain types of products are recommended.

There are many ways to solve the problem of data deviation. Below I will introduce a method based on matrix decomposition. This method converts user behavior data into a user-item rating matrix, then decomposes the matrix to obtain the hidden features of users and items, and finally makes recommendations.

First, we need to collect user behavior data, such as user ratings of items or click behavior. Suppose we have a user rating matrix R, in which each row represents a user, each column represents an item, and the elements in the matrix represent the user's rating of the item.

Next, we can use the matrix decomposition algorithm to generate hidden features of users and items. Specifically, we can use methods such as singular value decomposition (SVD) or gradient descent to decompose the rating matrix R. Assuming that the user's hidden feature matrix is ​​U and the item's hidden feature matrix is ​​V, then user u's rating of item i can be calculated through the inner product, that is, Ru = U[u] * V[i].

Next, we can train the model by minimizing the reconstruction error of the rating matrix R and the user and item hidden feature matrices. Specifically, we can use mean square error (MSE) as the loss function to optimize model parameters through gradient descent and other methods.

Finally, we can use the learned hidden features of users and items to make recommendations. For a new user, we can use the user's hidden features and the hidden features of the items to calculate the user's predicted rating for each item, and then recommend the items with the highest ratings to the user.

The following is a simple Python code example that demonstrates how to use matrix decomposition to solve the data bias problem:

import numpy as np

# 构造用户评分矩阵
R = np.array([[5, 4, 0, 0], [0, 0, 3, 4], [0, 0, 0, 0], [0, 0, 0, 0]])

# 设置隐藏特征的维度
K = 2

# 使用奇异值分解对评分矩阵进行分解
U, s, Vt = np.linalg.svd(R)

# 只保留前K个奇异值和对应的特征向量
U = U[:, :K]
V = Vt.T[:, :K]

# 计算用户和物品的隐藏特征向量
U = U * np.sqrt(s[:K])
V = V * np.sqrt(s[:K])

# 构造新用户
new_user = np.array([3, 0, 0, 0])

# 计算新用户对每个物品的预测评分
predicted_scores = np.dot(U, V.T)

# 找出预测评分最高的几个物品
top_items = np.argsort(predicted_scores[new_user])[::-1][:3]

print("推荐给新用户的物品：", top_items)

In summary, the data bias problem in intelligent recommendation systems is required by intelligent algorithms an important problem to solve. Through methods such as matrix decomposition, we can transform user behavior data into hidden features of users and items, thereby solving the problem of data bias. However, this is only one way to solve the problem of data bias, and there are many other methods worthy of further study and exploration.

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