Revealing the important role of Python in recommendation system development

Revealing the important role of Python in the development of recommendation systems

Recommendation systems have become an indispensable part of today’s Internet era, for e-commerce, social media, music and For various applications such as video platforms, the role of recommendation systems is self-evident. In the development process of recommendation systems, Python, as an efficient and flexible programming language, plays an important role. This article will reveal the important role of Python in the development of recommendation systems, and attach sample code.

1. Data processing and cleaning
   Data processing and cleaning in the recommendation system is an important and time-consuming process. Python's Pandas library makes it easy to process and clean large-scale data sets. Pandas provides a wealth of data structures and processing tools, such as DataFrame, which can easily filter, slice, and merge data. The following is a simple example:

import pandas as pd

# 读取数据
data = pd.read_csv("data.csv")

# 打印数据前5行
print(data.head())

# 数据清洗
# 删除空值
data.dropna()

# 数据处理
# 数据转换
data["price"] = data["price"].apply(lambda x: float(x.replace("$", "")))

# 数据筛选
filtered_data = data[data["price"] < 100]

# 打印筛选后的数据
print(filtered_data.head())

2. Feature extraction and representation
   In recommendation systems, feature extraction and representation are very important tasks. Python's machine learning library scikit-learn provides rich feature extraction and representation methods. For example, text data can be converted into numeric feature vectors using the TF-IDF method. Examples are as follows:

from sklearn.feature_extraction.text import TfidfVectorizer

# 文本数据
text_data = [
    "Python is a popular programming language",
    "Machine learning is an important part of AI",
    "Python and Machine learning are closely related"
]

# 使用TF-IDF方法提取特征
vectorizer = TfidfVectorizer()
features = vectorizer.fit_transform(text_data)

# 打印特征向量
print(features.toarray())

3. Model training and evaluation
   In the recommendation system, model selection and training are key steps. The machine learning library scikit-learn in Python provides a rich set of machine learning models and evaluation methods. The following is an example of a user-based collaborative filtering recommendation model:

from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import train_test_split

# 用户-物品评分矩阵
rating_matrix = [[5, 3, 0, 1],
                 [4, 0, 0, 1],
                 [1, 1, 0, 5],
                 [1, 0, 0, 4]]

# 切分训练集和测试集
train_matrix, test_matrix = train_test_split(rating_matrix, test_size=0.2)

# 计算用户相似度
user_similarity = cosine_similarity(train_matrix)

# 预测用户对物品的评分
def predict(user_id, item_id):
    similarity_sum = 0
    score_sum = 0
    for u_id in range(len(train_matrix)):
        if train_matrix[u_id][item_id] != 0:
            similarity_sum += user_similarity[user_id][u_id]
            score_sum += (user_similarity[user_id][u_id] * train_matrix[u_id][item_id])
    return score_sum / similarity_sum if similarity_sum != 0 else 0

# 对测试集进行评估
total_error = 0
for user_id in range(len(test_matrix)):
    for item_id in range(len(test_matrix[user_id])):
        if test_matrix[user_id][item_id] != 0:
            predicted_score = predict(user_id, item_id)
            error = abs(predicted_score - test_matrix[user_id][item_id])
            total_error += error

# 打印评估结果
print("Mean Absolute Error:", total_error / len(test_data))

In summary, Python plays an important role in the development of recommendation systems. Through Python's data processing and cleaning, feature extraction and representation, model training and evaluation and other functions, we can efficiently develop and optimize recommendation systems. I hope this article will be helpful to everyone in using Python in recommendation system development.

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