Unsupervised Learning for Recommender Systems

Duration: 7 min

This module delves into the application of unsupervised learning techniques for building recommender systems. We will explore algorithms like K-Means, DBSCAN, Hierarchical Clustering, PCA, t-SNE, and Autoencoders, understanding their implementation and utility in creating effective recommendation engines.

K-Means Clustering

K-Means is a popular clustering algorithm used to group similar items together. It is often used in recommender systems to identify clusters of users or items with similar characteristics. The algorithm iteratively assigns data points to clusters and updates the centroids until convergence.

from sklearn.cluster import KMeans
import numpy as np

# Sample data
data = np.array([[1, 2], [1, 4], [1, 0],
                 [4, 2], [4, 4], [4, 0]])

# Apply K-Means
kmeans = KMeans(n_clusters=2, random_state=0).fit(data)

# Get cluster labels
labels = kmeans.labels_
print(labels)

Try it in Google Colab:

[0 1 0 1 1 1]

DBSCAN Clustering

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. It groups together points that are packed closely together, marking as outliers points that lie alone in low-density regions. DBSCAN is useful in recommender systems for identifying dense regions of user preferences.

from sklearn.cluster import DBSCAN
import numpy as np

# Sample data
data = np.array([[1, 2], [1, 4], [1, 0],
                 [4, 2], [4, 4], [4, 0],
                 [8, 2], [8, 4], [8, 0]])

# Apply DBSCAN
dbscan = DBSCAN(eps=3, min_samples=2).fit(data)

# Get cluster labels
labels = dbscan.labels_
print(labels)

💡 Tip: When using DBSCAN, carefully choose the eps and min_samples parameters to avoid over-clustering or under-clustering.