It's commonly seen, regardless of the type of content or experience, to use recommendation systems based on User Behavior thanks to the countless algorithms available.
The easiest example given to students is usually related to the recommendation systems in shopping platforms like Amazon, where they track what you've previously ordered, what you regularly eyeball, what users like YOU have bought together your location and calendar holidays that might have impacts on your preferences, and a lot of other data that gets tracked with whatever you may do on their platform (or even off of it).
In terms of music, however, giant platforms like Spotify are doing this really well. This is my attempt to see what data insights can be gathered if I would implement algorithms that click for me in this use case, without first analysing what they're using.
Call this a random experiment.
Fair warning, the content being discussed is being used for a team project I'm involved in, so my motive here isn't to directly display the outcomes of these algorithms, but to share a possible pipeline to be further explored and criticized.
These algorithms, that players like Spotify are using, are designed to curate playlists, suggest songs, and recommend entire albums based on user preferences and listening habits.
However, as we delve deeper into the realm of artificial intelligence (AI) and machine learning (ML), it becomes clear that these systems are far more sophisticated than they appear.
They combine advanced data processing techniques with cutting-edge AI architectures to deliver personalized music experiences.
In this article, we will explore the intricate workings of modern music recommendation systems, focusing on how AI and ML intersect to create algorithms capable of understanding user emotions, preferences, and even cultural contexts.
Here's a brief on my initial understanding about metrics that matter, and what I'll be doing differently.
1. User interactions with music tracks.
2. Genres that the user goes back to most of the time (quantitatively)
3. Artists that make music which are in the user's preferred genres.
4. The number of listeners that an artist gets during a time period, along with geographic factors.
5. Occasions, Seasons, or External factors that might contribute to the habits of the mass users.
6. The popularity of certain music tracks.
7. The popularity of certain genres, especially as a function of qualities like age and geography.
(and more).
Here's what I think requires more experimentation beyond the normal statistic play, and this goes deeper into music theory.
1. Instead of focusing directly on music genres, why not focus on the key elements within the music tracks ranging from statements / lyrics / tempo and more.
2. Allow users the option to have more interactivity with their artists and music, beyond the usual, and use this as real-time bias inputs.
3. Instead of the usual artist metrics that define their success and will further choose how successful they can be due to the recommendation system algorithm designs, let's focus more on user's support and their popularity velocity rather than volume.
But there's more to explore.
The Data Flow & Preprocessing: Understanding Raw Inputs
At the heart of any recommendation system lies raw data, which is user interactions with a platform. For music platforms like Spotify or Apple Music, this data is typically in the form of timestamps, play counts, skips, and other listening behaviors. Not only that, but also how users interact with the entire app and the patterns involved in it. But before we can harness this data to make recommendations, it needs to be processed and transformed into a usable format.
Just for the sake of this experiment, I'll be using basic libraries (Pandas, NumPy) to clean and process real-time interaction data from dummy users, tracks (50 mock tracks), and dummy artists.
Here's some easy to comprehend pseudocode so you can visualize it all interact together easily:
# ---------------------------
# Sample Data Ingestion
# ---------------------------
def ingest_data():
"""
Simulate or load data from:
- User interactions (e.g., play counts, skips, timestamps)
- Audio files (raw music signals)
- Contextual metadata (device, location, mood indicators)
"""
user_interaction_data = load_user_interaction_logs()
audio_files = load_audio_files()
contextual_data = load_contextual_metadata()
return {
'user_interactions': user_interaction_data,
'audio': audio_files,
'context': contextual_data
}
User Behavior AKA Human Interactions with Music
User behavior on a music platform is a rich source of data for recommendation systems.
Timestamps tell us when a user listens to a song or album, allowing the system to track trends in listening habits over time.
Play counts can indicate popular songs or artists within a user’s library.
Skips provide insight into which tracks might be challenging for listeners to appreciate, or what they blatantly dislike.
This is all information that will help us to refine recommendations by avoiding similar tracks in that user's recomendations.
Beyond these basic metrics, users often leave contextual clues through their environment and mood. For example, listening to certain music during specific times of the day (e.g., early morning or late at night) can indicate preferences for nighttime vs. daytime genres with different moods.
Additionally, devices used smartphones, tablets, or desktop computers affect the user experience and could influence recommendations due to commonly seen usage. For example, larger screens usually support work enviroments whereas smaller devices indicate a casual or intimate atmosphere.
# ---------------------------
# Basic Preprocessing & Feature Engineering
# ---------------------------
def preprocess(data):
"""
Clean and align data.
- Handle missing values, normalize timestamps, extract session info.
"""
preprocessed_user_data = clean_user_data(data['user_interactions'])
preprocessed_audio = preprocess_audio(data['audio'])
preprocessed_context = clean_context(data['context'])
return {
'user': preprocessed_user_data,
'audio': preprocessed_audio,
'context': preprocessed_context
}
Audio Signals and Feature Engineering
Raw audio signals are the physical manifestation of music. These signals, captured as sound waves, vary in pitch, tempo, timbre, and other acoustic features.
To make sense of these raw audio files, we need to extract meaningful representations through a process called feature extraction, which means, as you'd expect, loads of more data to be analysed.
Feature extraction involves identifying key characteristics within the audio signal that can help distinguish between different songs or artists. For example, a deep jazz piece might be characterized by intricate harmonies and complex rhythms and deep lyrics, whereas a pop song might have a catchy melody and repetitive beats with iconic lyrics.
Convolutional Neural Networks (CNNs) are often used for this purpose, as they can detect these patterns within audio signals.
But the interesting thing to be analysed here is not just the elements inside the music, but how those elements affect listeners and their habits.
What if a user likes listening to Country and Pop, not because of their similarity in beats or vibes or acoustics, but because of the similarity in the lyrics.
Where basic regression and prediction could've been used with a bit of analysis in user patterns, and with room for adaptive learning for new behavior as the user goes along; we're instead using a much more complex pipeline to deal with these latent elements.
# ---------------------------
# Audio Feature Extraction using CNNs
# ---------------------------
def CNN_audio_extraction(audio_data):
"""
Pass audio signals through a CNN to extract low-level features.
"""
cnn_model = load_pretrained_CNN() # or train a CNN on audio datasets
audio_features = cnn_model.predict(audio_data)
return audio_features
Contextual Metadata: The Human Element
Contextual metadata adds another layer of complexity to the data flow. This includes information about when the user listened to a track: time of day, day of the week, or seasonality effects (e.g., holidays); as well as device usage and location.
For example, listening to music while commuting might indicate a preference for background tracks that don’t interfere with conversation.
This contextual metadata is crucial because it allows recommendation systems to account for external factors beyond just the audio itself. A user who listens to a particular song during their morning commute might prefer a more upbeat genre compared to someone who listens at night.
But for further understanding this region of the discussion, we'd need to explore the realm of psychology and why humans do things. Meaning, that we'll just mock it all for now.
# ---------------------------
# Context Fusion with Transformers & Contrastive Learning
# ---------------------------
def transformer_fusion(audio_features, contextual_data):
"""
Fuse CNN-extracted audio features with contextual metadata via a Transformer.
"""
transformer_model = load_transformer_model()
fused_features = transformer_model.forward(audio_features, contextual_data)
return fused_features
def contrastive_learning(fused_features):
"""
Refine multimodal representations to distinguish nuanced emotional states.
"""
contrastive_model = load_contrastive_model()
refined_features = contrastive_model.train_and_transform(fused_features)
return refined_features
The Graph-Based Modeling & Temporal Dynamics to Understand Complex Relationships
Once we’ve extracted features from user behavior, audio signals, and contextual metadata, the next step is to model these data points in a way that captures their relationships (take it easy, before it gets confusing). This is where graph-based modeling comes into play.
I've previously played around with Graph databases (Neo4j) for another personal project and from that point on, whenever I'm doing anything related to modelling relationships between data points or entities, I instinctively pray to be able to use them again.
Constructing the Heterogeneous Graph
A heterogeneous graph represents multiple types of nodes (users, songs, artists) and edges (interactions between them). For example:
Nodes : Can represent users, songs, or artists.
Edges : Represent interactions like “listens to,” “favorites,” or “buys.”
This structure allows the recommendation system to capture complex relationships within the ecosystem. For instance, a user might prefer an artist based on shared tastes with friends, or they might discover new music through listening habits (how they use the platform).
It's also possible for entire communities (clusters) of users to share patterns or habits because of shared listening preferences (oh how the turn-tables).
Temporal Graph Neural Networks (TGNN) for More Context
Over time, user preferences and listening behaviors change, so static graph models aren’t sufficient for dynamic recommendations.
Temporal Graph Neural Networks (TGNN) address this by incorporating temporal information into the graph structure. For example, if a user frequently listens to jazz tracks during their morning commute in December, the system can infer that they might prefer colder-weather-themed music in the future.
Applying TGNN after initial feature extraction allows the recommendation system to model how user preferences evolve over time. This is essential for delivering timely and relevant recommendations.
Graph Attention Networks (GATs) to Highlight Influential Interactions
Graph Attention Networks (GATs) go a step further by assigning weights to edges based on their importance. For example, a rare “likes” interaction might be more influential than frequent “skips.” By focusing on these influential interactions, the recommendation system can make more informed decisions.
Now going back to this project I'm working on. The motive is to allow more interactivity beyond the usual interactions being monitored.
This would range from, user's directly supporting their favorite artists like during award shows, but more regularly like for leaderboard stats.
This approach is particularly useful for filtering out noise in user behavior data, identifying patterns that might otherwise go unnoticed.
# ---------------------------
# Graph Construction and Modeling
# ---------------------------
def construct_graph(preprocessed_data, refined_features):
"""
Build a heterogeneous graph where:
- Nodes: Users, Tracks, Artists.
- Edges: Interactions (plays, likes, skips) weighted by refined features.
"""
graph = Graph()
graph.add_nodes_from(preprocessed_data['user'], type='user')
graph.add_nodes_from(preprocessed_data['audio'], type='track')
graph.add_nodes_from(get_artist_list(preprocessed_data['audio']), type='artist')
graph.add_edges(preprocessed_data['user'], preprocessed_data['audio'], refined_features)
return graph
def apply_TGNN(graph):
"""
Apply Temporal Graph Neural Network to incorporate time-evolving preferences.
"""
tgnn_model = load_TGNN_model()
temporal_graph = tgnn_model.process(graph)
return temporal_graph
def apply_GAT(temporal_graph):
"""
Refine graph by using Graph Attention Networks to highlight influential interactions.
"""
gat_model = load_GAT_model()
weighted_graph = gat_model.forward(temporal_graph)
return weighted_graph
Adaptive Learning & Latent Feature Discovery for Personalizing Recommendations
With a robust graph model and feature extraction pipeline in place, the next step is to discover latent features within the data. These are hidden patterns or characteristics that aren’t immediately apparent but can significantly influence user preferences, and the main focus in this discussion is to learn how to use these latent features.
Variational Autoencoders (VAEs): Discovering Hidden Representations
VAEs are a type of generative model that can uncover latent features within high-dimensional data. By training on user behavior and audio features together, VAEs can generate representations that highlight underlying patterns in the data: patterns that traditional collaborative filtering might miss.
These hidden representations are then used to improve recommendation quality by ensuring diversity in the suggestions provided.
What They Do: Discover hidden (latent) patterns in the data that traditional collaborative filtering might miss.
Why Use Them: Enhance the diversity of recommendations by uncovering subtle correlations and preferences within user behavior and content features.
Order in Pipeline: Can be applied either before or in parallel with RL to provide enriched latent features that feed into the decision-making process.
# ---------------------------
# Latent Feature Discovery using VAE
# ---------------------------
def VAE_latent_extraction(features):
"""
Uncover latent patterns using a Variational Autoencoder.
"""
vae_model = load_VAE_model()
latent_features = vae_model.encode(features)
return latent_features
Reinforcement Learning (RL): Continuous Improvement
Reinforcement learning provides an elegant solution for continuously improving recommendations. By treating the recommendation process as a sequential decision-making task, RL allows the system to learn and adapt in real time based on user feedback.
For example, if a user consistently rates a recommended track highly, the system reinforces that recommendation, making it more likely to appear again. Conversely, if a recommendation is poorly received, the system adjusts its algorithm to avoid similar suggestions in the future.
By treating user interactions as nodes and listening habits as edges, RL can dynamically adjust weights to prioritize relevant tracks, creating a system that personalizes listening experiences effectively.
But, how does this fall into place in this project (not just the piepline)? Reinforcement learning, for those of you who aren't too familiar with the implementation, involves creating an environment, an agent who can explore and interact with the environment, and a reward system to foster learning for the agent.
What we're doing is making music listening more interactive, or gamified if you'd prefer. The users and artists are agents, the platform is the environment and the reward system must be developed for a more intelligent recommendation model. This is a fun part of the experimentation.
# ---------------------------
# : Adaptive Learning via Reinforcement Learning
# ---------------------------
def RL_adaptive_module(weighted_graph, latent_features):
"""
Use Reinforcement Learning to update recommendations based on continuous feedback.
"""
rl_agent = load_RL_agent()
# Define state combining graph insights and latent representations.
state = combine_features(weighted_graph, latent_features)
# Compute reward based on simulated or real feedback.
reward = compute_reward(state)
# Update agent policy and output recommendations.
recommendations = rl_agent.update_policy(state, reward)
return recommendations
Multimodal Emotion Detection for Human Elements
While raw audio signals provide valuable insights, they often lack context. For example, a song with high energy might be liked by some users but disliked by others based on their mood or environment.
To address this challenge, researchers are exploring ways to integrate emotion detection into recommendation systems.
Function: Extract and refine audio-based emotional cues, then fuse these with user context to produce an emotion-aware representation.
Why Use Them: Emotionally resonant recommendations are more likely to enhance user satisfaction and retention.
Order in Pipeline: These components operate early in the pipeline (during feature extraction) and feed their output into the graph and RL modules to inform recommendation decisions.
Integrated CNNs, Transformers, & Contrastive Learning: Enhancing Audio Representations
Convolutional Neural Networks (CNNs) excel at identifying local patterns in audio signals, while Transformer architectures are adept at capturing long-range dependencies. Combining these with contrastive learning, which are techniques that refine representations by distinguishing between similar but distinct features, allows the system to create nuanced audio representations.
For example, a user who frequently listens to sad music during late-night commutes might be more likely to appreciate songs with certain emotional tonalities or structures.
By integrating emotion detection into the feature extraction pipeline, the recommendation system can make more informed decisions based on both objective audio data and subjective emotional cues.
Explainable AI (XAI) to Make Recommendations Transparent
One of the criticisms of modern recommendation systems is their “black box” nature. Users often don’t understand why a particular track or artist was recommended, which can lead to mistrust in the system. To address this, researchers are exploring ways to make these algorithms more transparent.
I decided to use generic nlp to translate the output of XAI for easy comprehension in the long run.
Role of XAI Across the Pipeline: Translating Complex Decisions into Understandable Insights
Explainable AI (XAI) techniques provide transparency by interpreting model decisions at every stage of the recommendation process:
Feature Importance : Identifying which factors (e.g., mood, device usage) contribute most significantly to a recommendation.
Model Explanations : Providing clear rationales for why specific tracks or artists are recommended.
By overlaying XAI techniques on each module—feature extraction, graph modeling, adaptive learning, and emotion detection—the recommendation system becomes more trustworthy and user-friendly.
# ---------------------------
# Explainable AI (XAI) Integration
# ---------------------------
def XAI_module(recommendations, weighted_graph):
"""
Generate interpretable insights on recommendation decisions.
"""
xai_model = load_XAI_tool()
explanation = xai_model.explain(recommendations, weighted_graph)
return explanation
A Unified Approach:
The final piece of the puzzle is integrating all these components into a unified workflow. By combining feature extraction with graph-based modeling, temporal dynamics, adaptive learning, and XAI, the recommendation system can deliver dynamic, context-aware, and emotionally resonant suggestions.
The greatest part is the fluidity in the system, where the behavior isn't simply quanitified and modelled, but is understood deeply, with accomodations for human behavior and the realm of psychology where I shall not trespass for now.
Sequential Flow:
Raw Inputs : User behavior, audio signals, contextual metadata.
Feature Extraction : CNNs for audio features; contrastive learning to refine representations.
Graph Construction & Temporal Modeling : Heterogeneous graph with TGNN and GATs.
Latent Feature Discovery : VAEs for hidden patterns.
Adaptive Recommendations : RL for dynamic updates; XAI for transparency.
Parallel/Overlapping Flow:
Multimodal emotion detection operates concurrently with other feature extraction steps, enhancing node features in the graph.
XAI techniques are integrated continuously to ensure real-time interpretation of outputs from each module.
The Overall Usefulness: A Balanced Approach to Music Recommendations
The integration of AI and ML into music recommendation systems offers immense potential for improving user satisfaction and engagement. By leveraging advanced data processing techniques, dynamic graph models, adaptive learning algorithms, and explainable AI methods, these systems can deliver highly personalized and context-aware recommendations.
However, as with any technology, challenges remain:
Data Quality : Ensuring that raw audio signals are high quality and representative of diverse listening habits. This means that you'll require a fully functioning music streaming platform with extractable insights and registered artists (because the artists themselves cause fluctutions in data with their existence.)
Privacy Concerns : Protecting user data while making recommendation systems more transparent.
Despite these challenges, the benefits—enhanced personalization, improved engagement, and greater satisfaction—are undeniable. As AI and ML continue to evolve, we can expect even more sophisticated music recommendations that deeply resonate with users on multiple levels.
Conclusion
The fusion of AI and musicology represents a groundbreaking advancement in music recommendation systems. By combining cutting-edge techniques like CNNs, Transformers, GATs, VAEs, and reinforcement learning, these systems can now deliver dynamic, context-aware, and emotionally resonant suggestions that go beyond mere entertainment.
In the coming years, as our understanding of human behavior and preference evolves alongside advancements in AI technology, music platforms will continue to evolve. With careful attention to both the technical and emotional dimensions of user experience, we can build systems that not only satisfy users but also deepen their connection to the music they love.
As the project develops, I'll share updates progressively, even on github.
Thanks for the attention span!
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