An In-Depth Recommendation System Case Study

Recommendation systems power much of the web—curating what products you see, what movies Netflix suggests, or which articles appear in your feed. In this MDX blog, we'll walk through a detailed case study of building two types of recommenders:

• Content-Based (based on item attributes)
• Collaborative Filtering (based on user interactions)

This guide is designed for beginner to intermediate practitioners who know some Python, have a bit of data science background, and are ready to tackle real-world recommender challenges.

Table of Contents

1. Introduction
2. Dataset & Setup
3. Exploratory Data Analysis (EDA)
4. Content-Based Recommender
5. Collaborative Filtering Recommender
6. Evaluation & Reliability
7. Conclusion & Next Steps

Introduction

A Recommendation System suggests items (movies, products, songs, etc.) to users based on their preferences or item similarities. Generally, two main categories are used:

1. Content-Based: Focuses on item descriptions (genres, tags, textual data). If a user liked an item, the system recommends other items with similar attributes.
2. Collaborative Filtering (CF): Focuses on user behaviors. It finds patterns in user–item interactions (ratings, clicks) and recommends items that similar users like.

We'll illustrate both approaches with an example dataset—commonly referencing movies for clarity but applicable to many domains (e-commerce products, music, news articles, etc.).

Dataset & Setup

Sample Movie Data

We assume two CSV files:

1. movies.csv: Contains columns like:

• movieId: Unique identifier
• title: Movie name
• genres: Pipe-separated genres (e.g., "Action|Adventure|Sci-Fi")

2. ratings.csv: Contains columns like:

• userId: Unique identifier for users
• movieId: Matches the IDs in movies.csv
• rating: Numerical rating (e.g., 1–5)
• timestamp: When the rating was given

Note: For your own project, adapt any real or synthetic dataset with user–item interactions.

Install & Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

!pip install scikit-learn
!pip install surprise

Exploratory Data Analysis (EDA)

df_movies = pd.read_csv('movies.csv')
df_ratings = pd.read_csv('ratings.csv')

# Check basic info
df_movies.info()
df_ratings.info()

• Missing Values: See if there are any columns with NaN.
• Data Types: Confirm columns like rating are numeric, movieId is integer, etc.

Quick Stats

    print(df_ratings['rating'].describe())

• Helps you see average rating, distribution, etc.