Setting Content-Based Recommendations Baseline with Linear Regression

In previous lessons, you learned about content-based recommendation systems and how they rely on user and item profiles. We covered how to extract content features such as likes, clicks, and genres, and how to compute similarities using straightforward methods like the dot product. This lesson will build on those foundations to guide you through a more complex example, using advanced techniques like regression models to generate recommendations.

We'll explore how to simulate user preferences, calculate genre similarities, and predict song ratings, offering you a glimpse into the practical applications of these systems in real-world scenarios, such as music streaming services. Let's dive into this sophisticated example step by step.

As a reminder from our previous lessons, let's quickly revisit how to load and merge datasets. We begin by using Python's pandas library to read from JSON files and create a merged DataFrame that contains both track and author information. Here's a code block demonstrating this process:

import pandas as pd

# Load data from JSON files
tracks_df = pd.read_json('tracks.json')
authors_df = pd.read_json('authors.json')

# Merge the dataframes on the common 'author_id' field
merged_df = pd.merge(tracks_df, authors_df, on='author_id', how='inner')

By executing this code, we create a unified view of our music tracks, integrating both track details and author information, which will serve as a foundation for our recommendation system.

To offer personalized recommendations, we need to simulate user preferences. Let's define a hypothetical user's listening history, quantifying their genre preferences and listening behavior.

# Simulate user listening history or preferences
user_features = {
    "rock_preference": 5,   # On a scale of 1-5
    "pop_preference": 4,    # On a scale of 1-5
    "jazz_preference": 2,   # On a scale of 1-5
    "listens": 50,          # Total listens
    "likes": 30             # Total likes
}

# Create a profile for the user
user_profile = pd.DataFrame([user_features])

Here, we've created a simple user profile indicating that our hypothetical user enjoys rock the most, followed by pop, and has a moderate affinity for jazz. This profile will be used to tailor recommendations to their tastes.