Content Features Extraction in 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 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
[
    {
        "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 track to its author using author_id field.

By using pandas, a powerful data manipulation library in Python, we can load these datasets into dataframes. Here's a quick reminder of how to do that:

import pandas as pd

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

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.