Embeddings Explained: The Hidden Engine Behind AI Search, Recommendations, and More

A practical, no-fluff guide to understanding how embeddings work, and why every AI engineer should care.

Generated by AI.

Introduction

I wrote this article for myself first.

When I started diving into embeddings, I wanted to have a clear understanding on what these word means in AI context. So I took notes and put everything together in a way that actually made sense to me.

Now I’m making it available here, because if it helped me, it might help you too.

Whether you’re an AI/ML engineer, a curious developer, or just someone trying to understand how search engines and recommendation systems actually work under the hood, this one’s for you. Let’s break it down.

What is embedding?

Embedding represents objects such as text, image, or audio as points in a continuous vector space where the location of those points are semantically meaningful to ML algorithms. They are stored as vectors in mathematical form according to the factors or traits each one may or may not have.

Embedding is a critical tool for AI/ML Engineers who build text and image search engines, chatbots, recommendation systems, etc.

How embedding works

The embedding model converts data into numerical format. For example, images are converted into pixel values or graphs into a numerical matrix. Then, those embedded objects are saved to a vector, which can be really large in number of dimensions depending on their complexity.

The closer an embedding is to another in this n-dimensional space, the more similar they are. Distribution similarity is determined by the length of vector points from one object to the other (Measured by Euclidean, cosine, or other).

# Example: "dad" and "mom" words represented as vectors:
"dad" = [0.1548, 0.4848, …, 1.864]
"mom" = [0.8785, 0.8974, …, 2.794]

Recommendation Embedding

A more complex example, which works by representing users and items (movies, products, articles) as high-dimensional vectors in a continuous vector space. These embeddings capture latent features that reflect users’ preferences and item characteristics.

The recommendation score is represented as:

RecommendationScore = UserEmbedding.ItemEmbedding

This formula generates the top-N recommendations for users. The highest predicted score wins.

Why use embedding

• Semantic representation: Captures semantic relationship and similarities, enabling models to understand and generalize better.
• Dimensionality reduction: High-dimensional data can be transformed into lower-dimensional representations making it computationally efficient and easier to work with.
• Improved generalization of models: By learning meaningful representations from data, models can generalize well to unseen examples, even if the trained data is limited.
• Effective visualization: Some techniques such as t-SNE can be applied to visualize high-dimensional embeddings in two or three dimensions, providing insights into the relationship and clusters in the data.
• Efficient training in neural networks: Facilitates back-propagation and optimization.

What objects can be embedded?

• Words
• Text
• Images
• Audio
• Graphs

How embeddings are created

1. Choose or train an embedding model: Select a pre-existing embedding model suitable for your data. For text, Word2Vec, Glove, or BERT. For images, VGG or ResNet.
2. Prepare your data: Format your data to match the expected format for the selected model. For text, tokenization and preprocessing. For images, resize and normalize.
3. Load or train the embedding model: For a pre-trained model, load its weights and architecture. For a new model, provide your prepared training data.
4. Generate embeddings: Use the trained or loaded model to generate embeddings.
5. Integrate embeddings into your application: Use your generated embeddings as features in your ML model, similarity search, recommendation, etc.

Real-world examples of embedding

Natural Language Processing (NLP)

• Word embedding in sentiment analysis.
• BERT for question answering.
• Text similarity with Doc2Vec.

Computer vision

• Image classification with CNNs.
• Image retrieval using CLIP.
• Facial recognition with FaceNet.

Recommender systems

• Collaborative filtering with embeddings.
• Product recommendations with word embeddings.

Cross-modal applications

• Multimodal translation with MUSE (Multilingual Universal Sentence Encoder).
• Cross-modal search using joint embeddings.

Anomaly detection

• Network anomaly detection with graph embeddings.
• Fraud detection with transaction embeddings.

Conclusion

Embeddings are one of those foundational concepts that quietly power almost everything in modern AI.

I put this guide together as my own personal reference, but I hope it gives you a solid starting point too. The best way to really understand embeddings is to get your hands dirty: pick a model, embed something, and see what happens.

More deep-dives like this are coming.

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References

[embed]What is Embedding? | IBM Embedding is a means of representing text and other objects as points in a continuous vector space that are…www.ibm.com

[embed]What are embeddings? | Learning Center Embeddings are numerical representations of data (text, images, etc.) in a high-dimensional vector space that capture…www.cloudflare.com