Content Based Recommendations

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.

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.json
[
    {
        "track_id": "001",
        "title": "Song A",
        "likes": 150,
        "clicks": 300,
        "full_listens": 120,
        "author_id": "A1"
    },
    {
        "track_id": "002",
        "title": "Song B",
        "likes": 200,
        "clicks": 400,
        "full_listens": 180,
        "author_id": "A2"
    },
    {
        "track_id": "003",
        "title": "Song C",
        "likes": 100,
        "clicks": 250,
        "full_listens": 95,
        "author_id": "A3"
    }
]

// authors.json
[
    {
        "author_id": "A1",
        "name": "Artist X",
        "author_listeners": 5000,
        "genre": "Rock"
    },
    {
        "author_id": "A2",
        "name": "Artist Y",
        "author_listeners": 8000,
        "genre": "Pop"
    },
    {
        "author_id": "A3",
        "name": "Artist Z",
        "author_listeners": 3000,
        "genre": "Jazz"
    }
]

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

Instead of working with plain JavaScript arrays, we will use Danfo.js DataFrames (dfd.DataFrame) for efficient data manipulation, similar to how data is handled in Python's pandas library.

Here’s how you can load and represent the datasets as DataFrames:

import { readFile } from 'fs/promises';
import dfd from 'danfojs-node';

// Load data from JSON files
const tracksData = JSON.parse(await readFile('tracks.json', 'utf-8'));
const authorsData = JSON.parse(await readFile('authors.json', 'utf-8'));

const tracks_df = new dfd.DataFrame(tracksData);
const authors_df = new dfd.DataFrame(authorsData);

After loading, the DataFrames tracks_df and authors_df look like this:

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.