Advanced Content Recommendations

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.

Before we proceed, let's recall how to represent user and track data using C++ data structures. Instead of using dictionaries or dataframes, we use struct to define the features of users and tracks, and arrays to store their values.

Here is how we can define user and track profiles in C++:

#include <iostream>
#include <vector>
#include <string>

struct UserProfile {
    int rock_preference;   // Scale 1-5
    int pop_preference;    // Scale 1-5
    int jazz_preference;   // Scale 1-5
    int listens;           // Total listens
    int likes;             // Total likes
};

struct Track {
    std::string name;
    std::string genre;
    int likes;
    int clicks;
    int full_listens;
    int author_listeners;
};

This setup allows us to store and manipulate user and track information efficiently in C++.

To offer personalized recommendations, we need to simulate user preferences. In C++, we can create a user profile by initializing a UserProfile struct with the desired values.

// Simulate user listening history or preferences
UserProfile user = {
    5,   // rock_preference
    4,   // pop_preference
    2,   // jazz_preference
    50,  // listens
    30   // likes
};

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.