Python machine learning project practice: teach you to build an intelligent recommendation system

The intelligent recommendation system is a recommendationalgorithm that is widely used in e-commerce, streaming media, social media and other fields. Its purpose is to provide users with personalized recommendation results and improve user satisfaction and participation. Intelligent recommendation systems are usually based on machine learning technology and learn the user's interests and preferences by analyzing the user's historical behavioral data. The system then recommends content or products to users that may be of interest to them based on these interests and preferences.

To build an intelligent recommendation system, you first need to collect and preprocess user data. This data can include users’ purchase records, browsing records, search records, click records, etc. This data can then be used to train a machine learning model that is able to predict a user's level of interest in different items.

In python, you can use some mature machine learning libraries to build recommendation systems, such as scikit-learn and surprise. scikit-learn provides many commonly used machine learning algorithms, while surprise is a library specifically used for building recommendation systems.

The following is a simple Python code example that demonstrates how to use scikit-learn to build a simple recommendation system:

import numpy as np
from sklearn.neighbors import NearestNeighbors

# Load the user-item interaction data
data = np.loadtxt("data.csv", delimiter=",")

# Create a Nearest Neighbors model
model = NearestNeighbors(metric="cosine", alGorithm="brute")

# Fit the model to the data
model.fit(data)

# Get recommendations for a user
user_id = 10
neighbors = model.kneighbors(data[user_id, :], n_neighbors=10)

# Print the recommended items
for item_id in neighbors[1]:
print(item_id)

This code first loads the user-item interaction data, and then creates a Nearest Neighbors model. This model uses cosine similarity as the similarity measure and uses a brute force algorithm to calculate the similarity. Then, the model is trained on the data. Finally, the code uses the model to get recommended items for a specific user.

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