Use Collaborative Filtering to Build a Movie Recommendation System with Qdrant

Time: 45 min	Level: Intermediate

Every time Spotify recommends the next song from a band you’ve never heard of, it uses a recommendation algorithm based on other users’ interactions with that song. This type of algorithm is known as collaborative filtering.

Unlike content-based recommendations, collaborative filtering excels when the objects’ semantics are loosely or unrelated to users’ preferences. This adaptability is what makes it so fascinating. Movie, music, or book recommendations are good examples of such use cases. After all, we rarely choose which book to read purely based on the plot twists.

The traditional way to build a collaborative filtering engine involves training a model that converts the sparse matrix of user-to-item relations into a compressed, dense representation of user and item vectors. Some of the most commonly referenced algorithms for this purpose include SVD (Singular Value Decomposition) and Factorization Machines. However, the model training approach requires significant resource investments. Model training necessitates data, regular re-training, and a mature infrastructure.

Methodology

Fortunately, there is a way to build collaborative filtering systems without any model training. You can obtain interpretable recommendations and have a scalable system using a technique based on similarity search. Let’s explore how this works with an example of building a movie recommendation system.

Implementation

To implement this, you will use a simple yet powerful resource: Qdrant with Sparse Vectors.

Notebook: You can try this code here

Setup

You have to first import the necessary libraries and define the environment.

import os
import pandas as pd
import requests
from qdrant_client import QdrantClient, models
from qdrant_client.models import PointStruct, SparseVector, NamedSparseVector
from collections import defaultdict

# OMDB API Key - for movie posters
omdb_api_key = os.getenv("OMDB_API_KEY")

# Collection name
collection_name = "movies"

# Set Qdrant Client
qdrant_client = QdrantClient(
    os.getenv("QDRANT_HOST"),
    api_key=os.getenv("QDRANT_API_KEY")
)

Define output

Here, you will configure the recommendation engine to retrieve movie posters as output.

# Function to get movie poster using OMDB API
def get_movie_poster(imdb_id, api_key):
    url = f"https://www.omdbapi.com/?i={imdb_id}&apikey={api_key}"
    data = requests.get(url).json()
    return data.get('Poster'), data

Prepare the data

Load the movie datasets. These include three main CSV files: user ratings, movie titles, and OMDB IDs.

# Load CSV files
ratings_df = pd.read_csv('data/ratings.csv', low_memory=False)
movies_df = pd.read_csv('data/movies.csv', low_memory=False)

# Convert movieId in ratings_df and movies_df to string
ratings_df['movieId'] = ratings_df['movieId'].astype(str)
movies_df['movieId'] = movies_df['movieId'].astype(str)

rating = ratings_df['rating']

# Normalize ratings
ratings_df['rating'] = (rating - rating.mean()) / rating.std()

# Merge ratings with movie metadata to get movie titles
merged_df = ratings_df.merge(
    movies_df[['movieId', 'title']],
    left_on='movieId', right_on='movieId', how='inner'
)

# Aggregate ratings to handle duplicate (userId, title) pairs
ratings_agg_df = merged_df.groupby(['userId', 'movieId']).rating.mean().reset_index()

ratings_agg_df.head()

Convert to sparse

If you want to search across numerous reviews from different users, you can represent these reviews in a sparse matrix.

# Convert ratings to sparse vectors
user_sparse_vectors = defaultdict(lambda: {"values": [], "indices": []})
for row in ratings_agg_df.itertuples():
    user_sparse_vectors[row.userId]["values"].append(row.rating)
    user_sparse_vectors[row.userId]["indices"].append(int(row.movieId))

Upload the data

Here, you will initialize the Qdrant client and create a new collection to store the data. Convert the user ratings to sparse vectors and include the movieId in the payload.

# Define a data generator
def data_generator():
    for user_id, sparse_vector in user_sparse_vectors.items():
        yield PointStruct(
            id=user_id,
            vector={"ratings": SparseVector(
                indices=sparse_vector["indices"],
                values=sparse_vector["values"]
            )},
            payload={"user_id": user_id, "movie_id": sparse_vector["indices"]}
        )

# Upload points using the data generator
qdrant_client.upload_points(
    collection_name=collection_name,
    points=data_generator()
)

Define query

In order to get recommendations, we need to find users with similar tastes to ours. Let’s describe our preferences by providing ratings for some of our favorite movies.

1 indicates that we like the movie, -1 indicates that we dislike it.

my_ratings = {
    603: 1,     # Matrix
    13475: 1,   # Star Trek
    11: 1,      # Star Wars
    1091: -1,   # The Thing
    862: 1,     # Toy Story
    597: -1,    # Titanic
    680: -1,    # Pulp Fiction
    13: 1,      # Forrest Gump
    120: 1,     # Lord of the Rings
    87: -1,     # Indiana Jones
    562: -1     # Die Hard
}

# Create sparse vector from my_ratings
def to_vector(ratings):
    vector = SparseVector(
        values=[],
        indices=[]
    )
    for movie_id, rating in ratings.items():
        vector.values.append(rating)
        vector.indices.append(movie_id)
    return vector

Run the query

From the uploaded list of movies with ratings, we can perform a search in Qdrant to get the top most similar users to us.

# Perform the search
results = qdrant_client.query_points(
    collection_name=collection_name,
    query=to_vector(my_ratings),
    using="ratings",
    limit=20
).points

Now we can find the movies liked by the other similar users, but we haven’t seen yet. Let’s combine the results from found users, filter out seen movies, and sort by the score.

# Convert results to scores and sort by score
def results_to_scores(results):
    movie_scores = defaultdict(lambda: 0)
    for result in results:
        for movie_id in result.payload["movie_id"]:
            movie_scores[movie_id] += result.score
    return movie_scores

# Convert results to scores and sort by score
movie_scores = results_to_scores(results)
top_movies = sorted(movie_scores.items(), key=lambda x: x[1], reverse=True)

# Create HTML to display top 5 results
html_content = "<div class='movies-container'>"

for movie_id, score in top_movies[:5]:
    imdb_id_row = links.loc[links['movieId'] == int(movie_id), 'imdbId']
    if not imdb_id_row.empty:
        imdb_id = imdb_id_row.values[0]
        poster_url, movie_info = get_movie_poster(imdb_id, omdb_api_key)
        movie_title = movie_info.get('Title', 'Unknown Title')
        
        html_content += f"""
        <div class='movie-card'>
            <img src="{poster_url}" alt="Poster" class="movie-poster">
            <div class="movie-title">{movie_title}</div>
            <div class="movie-score">Score: {score}</div>
        </div>
        """
    else:
        continue  # Skip if imdb_id is not found

html_content += "</div>"

display(HTML(html_content))

Recommendations

For a complete display of movie posters, check the notebook output. Here are the results without html content.

Toy Story, Score: 131.2033799 
Monty Python and the Holy Grail, Score: 131.2033799 
Star Wars: Episode V - The Empire Strikes Back, Score: 131.2033799  
Star Wars: Episode VI - Return of the Jedi, Score: 131.2033799 
Men in Black, Score: 131.2033799

On top of collaborative filtering, we can further enhance the recommendation system by incorporating other features like user demographics, movie genres, or movie tags.

Or, for example, only consider recent ratings via a time-based filter. This way, we can recommend movies that are currently popular among users.

Conclusion

As demonstrated, it is possible to build an interesting movie recommendation system without intensive model training using Qdrant and Sparse Vectors. This approach not only simplifies the recommendation process but also makes it scalable and interpretable. In future tutorials, we can experiment more with this combination to further enhance our recommendation systems.