Programming

Item-Based Collaborative Filtering in Python

A Practical Example of Item-Based Collaborative Filtering

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We will provide an example of Item-Based Collaborative Filterings by showing how we can find similar movies. There are many different approaches and techniques. We will work with the Singular Matrix Decomposition. You can find on-line good lectures about Matrix Factorization by Gilbert Strang (MIT), for example about LU and SVD Decomposition.

Item-Based Collaborative Filtering on Movies

We will work with the MovieLens dataset, collected by the GroupLens Research Project at the University of Minnesota.

import pandas as pd
import numpy as np
import sklearn
from sklearn.decomposition import TruncatedSVD


columns = ['user_id', 'item_id', 'rating', 'timestamp']
df = pd.read_csv('ml-100k/u.data', sep='\t', names=columns)


columns = ['item_id', 'movie title', 'release date', 'video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure',
          'Animation'…