Are you looking for ways to provide personalized recommendations to your target audience? A recommender system can help you achieve exactly that! Recommendation engines are widely used in various industries, such as e-commerce, movies, and music.
In this tutorial, you’ll learn how to build a recommender system with Python using collaborative filtering. Collaborative filtering is a technique that uses user feedback to recommend items based on users’ preferences. This tutorial will cover the following topics:
Introduction to Collaborative Filtering
Collaborative filtering is a technique that predicts a user’s preferences based on the preferences of other users. There are two main types of collaborative filtering:
- User-based collaborative filtering
- Item-based collaborative filtering
In user-based collaborative filtering, users with similar preferences are grouped together, and recommendations are made based on the ratings of similar users. In item-based collaborative filtering, similarities between items are calculated, and recommendations are made based on items that are similar to the ones the user has liked.
Building a Recommender System with Python
To build a recommender system with Python, you’ll need a dataset that contains user preferences. In this tutorial, we’ll use the MovieLens dataset, which contains ratings for various movies.
You can download the dataset from here: https://grouplens.org/datasets/movielens/
Once you’ve downloaded the dataset, you can start by importing the required Python libraries:
import pandas as pd import numpy as np from sklearn.metrics.pairwise import cosine_similarity
Next, you can read the dataset into a Pandas dataframe:
movies = pd.read_csv('movies.csv') ratings = pd.read_csv('ratings.csv')
You can then merge the two dataframes:
df = pd.merge(movies, ratings, on='movieId')
Now that you have the merged dataframe, you can create a pivot table that shows the ratings for each user and movie:
ratings_matrix = df.pivot_table(index='userId', columns='title', values='rating')
You can then calculate the cosine similarity between each pair of movies:
item_similarity_matrix = cosine_similarity(ratings_matrix.T)
This matrix contains similarity scores for each pair of movies. You can then use this matrix to make recommendations based on the movies the user has already rated:
user_ratings = ratings_matrix.loc[user_id].dropna() similarity_scores = item_similarity_matrix[title] weighted_scores = user_ratings \* similarity_scores recommendations = weighted_scores.sort_values(ascending=False).head(n)
Here, user_id is the ID of the user you’re making recommendations for, title is the title of the movie for which you’re making recommendations, and n is the number of recommendations you want to make.
In this tutorial, you learned how to build a simple recommender system with Python using collaborative filtering. Collaborative filtering is a powerful technique that can help you provide personalized recommendations to your target audience. By following the steps outlined in this tutorial, you can easily build your own recommender system and start making personalized recommendations to your users.
Want to learn more about Python, checkout the Python Official Documentation for detail.