Welcome to the lesson on preparing datasets for factorization machines. In this lesson, you will learn how to create a detailed dataset to be used in recommendation systems using factorization machines. Factorization machines are advanced models that capture complex interactions between different data features, making them powerful tools for making accurate recommendations.
Why focus on a structured dataset? A well-prepared dataset allows a factorization machine to learn meaningful relationships from the data, leading to better recommendation outcomes. This lesson will guide you through organizing your data in a format suitable for factorization machines.
Before diving into dataset preparation, let's briefly review how to read and understand our data files. You will work with three JSON files: tracks.json
, users.json
, and interactions.json
. We already saw some examples of how tracks.json
and users.json
might look like. Let's take a look at the interactions.json
file:
JSON1[ 2 { 3 "user_id": 1, 4 "track_id": 1, 5 "rating": 3 6 }, 7 { 8 "user_id": 1, 9 "track_id": 2, 10 "rating": 4 11 }, 12... more data 13]
For each pair of a user and a track that this user interacted with, the file keeps track of the rating that the user gave to this track.
Here is a consolidated code snippet to load these files:
Python1import json 2 3# Reading JSON files 4with open('tracks.json', 'r') as tracks_file: 5 tracks = json.load(tracks_file) 6 7with open('users.json', 'r') as users_file: 8 users = json.load(users_file) 9 10with open('interactions.json', 'r') as interactions_file: 11 interactions = json.load(interactions_file)
This code reads the JSON files and prints their contents. Real-world data typically needs to be loaded like this before further processing.
The user-item interaction matrix is a fundamental component of many recommendation systems. It's a simplified way to understand which user interacts with which item and how.
Imagine you have three users and three tracks. Each interaction can be represented using dummy variables that indicate whether a user interacted with a track and what their rating was. For example:
user1 | user2 | user3 | track1 | track2 | track3 | rating |
---|---|---|---|---|---|---|
1 | 0 | 0 | 1 | 0 | 0 | 3 |
0 | 1 | 0 | 0 | 1 | 0 | 4 |
In this table, 1
and 0
indicate the presence or absence of interaction between users and tracks. The rating
column shows the rating a user gave to a track. This representation allows us to define user-item pairs. For example, the first row is for user1
and track1
pair, and it tells us that user1
's rating for track1
is 3
.
To make our dataset more informative, we incorporate auxiliary features such as user preferences and item statistics. These enrich the data, providing a better foundation for the system to learn from. Let's consider an example:
user1 | user2 | user3 | track1 | track2 | track3 | track_likes | user_listening_avg | genre_similarity | rating |
---|---|---|---|---|---|---|---|---|---|
1 | 0 | 0 | 1 | 0 | 0 | 100 | 180 | 0.89 | 3 |
0 | 1 | 0 | 0 | 1 | 0 | 250 | 220 | 0.76 | 4 |
In the table, besides the user and track dummy variables and the rating
column, we have additional auxiliary features:
track_likes
: Indicates the number of likes a track has received, providing a popularity measure. For example, the first row is foruser1
-track1
pair, so thetrack_likes
in the first row is related totrack1
.user_listening_avg
: Reflects the average listening duration for a user, which can indicate user engagement. Similarly, the first row is foruser1
-track1
pair, so theuser_listening_avg
in the first row is related touser1
.genre_similarity
: Measures the similarity between a user's genre preferences and a track's genre using cosine similarity. This feature captures the alignment between user tastes and item characteristics. Again, in the same manner, the first row is foruser1
-track1
pair, so thegenre_similarity
in the first row is related to the similarity betweenuser1
andtrack1
.
These features enrich the dataset, allowing factorization machines to capture deeper insights and nuances within the data.
Consider the genre encoding for tracks and computing a similarity index between user preferences and track genres. For example, encoding could look like:
Python1from sklearn.metrics.pairwise import cosine_similarity 2import numpy as np 3 4# Create genre encoding 5genre_encodings = {"Jazz": [1, 0, 0], "Pop": [0, 1, 0], "Rock": [0, 0, 1]} 6 7# Example: Calculating genre similarity 8user_genre_array = np.array([0.5, 0.8, 0.3]) 9track_genre_array = np.array(genre_encodings["Jazz"]) 10genre_similarity = cosine_similarity([user_genre_array], [track_genre_array])[0, 0]
Here, we encode genres and calculate genre similarity using cosine_similarity
. This value reflects how closely user preferences match a track's genre, which can significantly influence recommendations.
In this part of the code, we create dummy variables for users and tracks. Dummy variables are binary indicators that represent whether a particular user or track is involved in the interaction.
Python1user_dummies = [1 if i == user_id else 0 for i in range(1, len(users) + 1)] 2track_dummies = [1 if i == track_id else 0 for i in range(1, len(tracks) + 1)]
user_dummies
: This list comprehension iterates over possible user indices. It assigns a1
if the current index matches theuser_id
from the interaction, otherwise0
. This creates a binary representation for user involvement.track_dummies
: Similarly, this creates a binary representation for track involvement by checking if the current index matches thetrack_id
.
Next, we extract specific features from both user and track data using their respective IDs.
Python1user = next(user for user in users if user['id'] == user_id) 2track = next(track for track in tracks if track['id'] == track_id) 3 4track_likes = track['likes'] 5user_listening_avg = user['time_listening_avg']
- We use the
next()
function to find the user and track objects that correspond to the current interaction's user and track IDs. track_likes
is extracted to indicate the number of likes a track has.user_listening_avg
is used to measure the average listening duration for a user.
This section calculates the genre similarity and consolidates all features into a single row.
Python1# Calculate genre similarity 2user_genre_array = np.array(list(user['genre_preferences'].values())) 3track_genre_array = np.array(genre_encodings[track['genre']]) 4genre_similarity = cosine_similarity([user_genre_array], [track_genre_array])[0, 0] 5 6# Combine all features 7row = user_dummies + track_dummies + [track_likes, user_listening_avg, genre_similarity, rating] 8data.append(row)
- Genre similarity: This is calculated using
cosine_similarity
to measure how closely user preferences match a track's genre. - Combining features: All dummy variables and features are consolidated into a single row, which is appended to the
data
list. This row contains user and track indicators, auxiliary features, and the interaction rating.
The final step is to gather all these elements into a structured data matrix.
Python1import pandas as pd 2 3# Prepare data 4data = [] 5 6for interaction in interactions: 7 user_id = interaction['user_id'] 8 track_id = interaction['track_id'] 9 rating = interaction['rating'] 10 11 # Create user and track dummy variables 12 user_dummies = [1 if i == user_id else 0 for i in range(1, len(users) + 1)] 13 track_dummies = [1 if i == track_id else 0 for i in range(1, len(tracks) + 1)] 14 15 # Extract user and track features 16 user = next(user for user in users if user['id'] == user_id) 17 track = next(track for track in tracks if track['id'] == track_id) 18 19 track_likes = track['likes'] 20 user_listening_avg = user['time_listening_avg'] 21 22 # Calculate genre similarity 23 user_genre_array = np.array(list(user['genre_preferences'].values())) 24 track_genre_array = np.array(genre_encodings[track['genre']]) 25 genre_similarity = cosine_similarity([user_genre_array], [track_genre_array])[0, 0] 26 27 # Combine all features 28 row = user_dummies + track_dummies + [track_likes, user_listening_avg, genre_similarity, rating] 29 data.append(row) 30 31# Define column names 32columns = ['user1', 'user2', 'user3', 'track1', 'track2', 'track3', 33 'track_likes', 'user_listening_avg', 'genre_similarity', 'rating'] 34 35# Create DataFrame 36df = pd.DataFrame(data, columns=columns)
This complete code snippet constructs a comprehensive data frame with all the necessary data for factorization machines in recommendation systems.
Congratulations! You have learned how to prepare a dataset for factorization machines by creating a comprehensive data matrix. This matrix serves as the foundation for designing an effective recommendation system.
In this lesson, you revisited loading JSON files, learned to represent user-item interactions with dummy variables, and incorporated auxiliary features to enrich the data. Understanding these components is crucial for designing more accurate and personalized recommendation algorithms.
Now, it's time for you to apply these skills in the upcoming practice exercises. Your journey into creating sophisticated recommendation systems begins with mastering these foundations. Good luck, and remember to reference this lesson as needed!