Content Based Recommendation Systems

Introduction to Content-Based Recommendation Systems

Welcome to the beginning of our journey into content-based recommendation systems. In the grand scope of recommendation technologies, these systems play a crucial role. They allow applications to suggest relevant items to users based on various content features, enhancing the user experience through personalization. Imagine a music app recommending songs based on the characteristics of songs that a user has liked or listened to in the past. That's the power of a content-based system!

In this lesson, we will delve into how content features are extracted to create efficient recommendations, setting a solid foundation for more advanced techniques.

Dataset Overview and Setup

Let's start by revisiting the datasets we will be working with: tracks.json and authors.json. These JSON files contain essential information about music tracks and artists, respectively. Here is an example of how this can work:

# tracks
[
    {
        "track_id": "001",
        "title": "Song A",
        "likes": 150,
        "clicks": 300,
        "full_listens": 120,
        "author_id": "A1"
    },
... more tracks
]
# authors
[
    {
        "author_id": "A1",
        "name": "Artist X",
        "author_listeners": 5000,
        "genre": "Rock"
    },
... more authors
]

Note that we link a track to its author using the author_id field.

Constructing DataFrames

In C++, we will use the DataFrame library to represent our tabular data. Unlike in some other environments, we will manually construct our dataframes by providing the data directly in the code. This approach gives us full control and clarity over the data structure.

Here is how we can manually create the dataframes for tracks and authors:

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

#include <DataFrame/DataFrame.h>

using namespace hmdf;

int main() {
    // Create tracks dataframe manually
    StdDataFrame<unsigned long> tracks_df;
    
    // Add index
    std::vector<unsigned long> track_indices = {0, 1, 2};
    tracks_df.load_index(std::move(track_indices));
    
    // Add track data
    std::vector<std::string> track_ids = {"001", "002", "003"};
    std::vector<std::string> titles = {"Song A", "Song B", "Song C"};
    std::vector<int> likes = {150, 200, 100};
    std::vector<int> clicks = {300, 400, 250};
    std::vector<int> full_listens = {120, 180, 95};
    std::vector<std::string> author_ids = {"A1", "A2", "A3"};
    
    tracks_df.load_column("track_id", std::move(track_ids));
    tracks_df.load_column("title", std::move(titles));
    tracks_df.load_column("likes", std::move(likes));
    tracks_df.load_column("clicks", std::move(clicks));
    tracks_df.load_column("full_listens", std::move(full_listens));
    tracks_df.load_column("author_id", std::move(author_ids));
    
    // Create authors dataframe manually
    StdDataFrame<unsigned long> authors_df;
    
    // Add index
    std::vector<unsigned long> author_indices = {0, 1, 2};
    authors_df.load_index(std::move(author_indices));
    
    // Add author data
    std::vector<std::string> author_ids_df = {"A1", "A2", "A3"};
    std::vector<std::string> names = {"Artist X", "Artist Y", "Artist Z"};
    std::vector<int> author_listeners = {5000, 8000, 3000};
    std::vector<std::string> genres = {"Rock", "Pop", "Jazz"};
    
    authors_df.load_column("author_id", std::move(author_ids_df));
    authors_df.load_column("name", std::move(names));
    authors_df.load_column("author_listeners", std::move(author_listeners));
    authors_df.load_column("genre", std::move(genres));
    
    // ... (rest of the code will go here)
    return 0;
}

After this step, the dataframes tracks_df and authors_df are ready and contain the following data:

tracks_df:

  track_id   title  likes  clicks  full_listens author_id
0      001  Song A    150     300           120        A1
1      002  Song B    200     400           180        A2
2      003  Song C    100     250            95        A3

authors_df:

  author_id      name  author_listeners  genre
0        A1  Artist X              5000   Rock
1        A2  Artist Y              8000    Pop
2        A3  Artist Z              3000   Jazz

These dataframes are tabular structures, similar to spreadsheets, where data can be easily processed and analyzed.

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