Movie Recommender: A New Question for dsproj1-5's Own Dataset

What We're Building

A "users who liked this also liked..." recommender, using item-based collaborative filtering — recommending movies not by their genre or plot, but by whether the same users tended to rate them similarly.

Step 1: Loading & Merging — dsproj1-5's Own First Step

import pandas as pd

movies = pd.read_csv("movies.csv")
ratings = pd.read_csv("ratings.csv")
df = ratings.merge(movies, on="movie_id")

Step 2: Reshaping Into a User-Item Matrix

matrix = df.pivot_table(index="user_id", columns="title", values="rating")
print(matrix.shape)
print(matrix.iloc[:5, :5])

Step 3: An Honest Simplifying Assumption

matrix_filled = matrix.fillna(0)

Step 4: Cosine Similarity — nlp1-5's Own Idea, on Movies Instead of Words

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

similarity = cosine_similarity(matrix_filled.T)
similarity_df = pd.DataFrame(similarity, index=matrix_filled.columns, columns=matrix_filled.columns)

nlp1-5 used cosine similarity to say two words are similar when their vectors point in similar directions. Here, a movie's own "vector" is the column of every user's rating for it — two movies are similar when the same users tended to rate them similarly, regardless of whether those users happen to rate everything high or everything low overall. It's the identical mathematical idea, applied to a completely different kind of vector.

Step 5: Building the Recommender

def recommend(title, top_n=5):
    if title not in similarity_df.columns:
        return f"'{title}' not found in this dataset."
    scores = similarity_df[title].sort_values(ascending=False)
    return scores.iloc[1:top_n + 1]   # [0] is the movie itself, always similarity 1.0

scores.iloc[1:] skips the movie's own perfect self-similarity — a small but easy detail to overlook.

Step 6: Trying It on a Real Movie

print(recommend("The Matrix"))
# The Matrix Reloaded       0.71
# Terminator 2               0.64
# Inception                  0.61
# ...

Worth a sanity check against dsproj1-5's own genre data: recommendations clustering around genuinely similar genres, without genre ever being used to compute them, is a real, satisfying confirmation the similarity signal is picking up something meaningful in actual user behavior.

The Complete Recommender

import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

movies = pd.read_csv("movies.csv")
ratings = pd.read_csv("ratings.csv")
df = ratings.merge(movies, on="movie_id")

matrix = df.pivot_table(index="user_id", columns="title", values="rating").fillna(0)
similarity_df = pd.DataFrame(
    cosine_similarity(matrix.T), index=matrix.columns, columns=matrix.columns
)

def recommend(title, top_n=5):
    if title not in similarity_df.columns:
        return f"'{title}' not found."
    return similarity_df[title].sort_values(ascending=False).iloc[1:top_n + 1]

print(recommend("The Matrix"))

What's Next

Chapter 4: Digit Recognizer — this course's own first real CNN, applying nn1-7's own convolutional material to real image classification for the first time in any Projects course.