How Embedding Can Accelerate Your Recommender System

Have you ever wondered how Airbnb suggests accommodations that suit you best? Or how can Spotify create a playlist that perfectly captures your musical tastes? 

The answer lies in the power of embedding, a technique that enhances recommender systems by capturing the complex relationships between users and the items they interact with.

In a recent study published in the Journal of Big Data, researchers found that using a deep neural network-based embedding approach improved the accuracy of a movie recommendation system by up to 20% compared to a traditional matrix factorization approach. 

The study also found that the neural network-based approach was more effective at capturing complex user-item interactions and could make accurate recommendations even for users with sparse data. It highlights the potential of embedding techniques to continue advancing the recommender systems field and enable more precise and personalized recommendations.

This article will explore how embedding enhances the recommender system and why it has become a paramount technique in the field. We will discuss the benefits of embedding techniques for the recommender systems and review the success stories.

What is Embedding?

Embedding is a technique used to represent users and items as low-dimensional vectors in a mathematical space. The vectors representing users and items in the embedding space are learned from data using machine learning techniques, such as matrix factorization, deep learning, or graph-based methods.

For instance, you can see the embedding technique in music recommendation systems, such as the one used by Spotify. In these systems, songs are represented as low-dimensional vectors in an embedding space, which captures the relationships between songs based on their musical features, such as tempo, rhythm, and melody.

When a user listens to a song or creates a playlist, the system uses the embeddings to recommend similar songs that the user might like. It is done by computing the similarity between the user’s listening history or playlist and the embeddings of other pieces in the system.

How Embedding Enhances Recommender System

Embedding is a powerful technique that can significantly enhance the performance of recommender systems. Thus, numerous corporations, such as Alibaba, Anghami, Spotify, Asos, Airbnb, and others, have been using this technique since 2018 to strengthen their businesses.

So, what limitations does the traditional recommender system approach have, and how do embeddings overcome these limitations?

Cold start problem

The cold start problem is a type of issue in recommendation systems that occurs when there is little or no information available about new items or users. In such cases, the recommendation system may struggle to provide accurate recommendations.

Embedding is a solution to the cold start problem. This technique represents items and users as low-dimensional vectors that capture their inherent features and relationships.

The recommendation system learns these embeddings through machine learning algorithms that analyze user behavior, item attributes, and other contextual information.


Collaborative filtering requires processing large amounts of problem-item interaction data, which can be computationally expensive and slow.

Embeddings can efficiently represent large sets of items and users, allowing recommendation systems to scale to millions or even billions of items and users.

Sparsity problem

Collaborative filtering relies on user-item interaction data to make recommendations. However, in many cases, the number of items a user interacts with is small compared to the total number of items available. It can lead to sparsity in the user-item interaction matrix, challenging identifying user patterns and similarities.

Embeddings can represent items or users in a high-dimensional space, where similar items or users are mapped to nearby points in the space. It allows recommendation systems to identify similarities and patterns among items or users, even if they have sparse interaction data.

Improved accuracy

Embeddings can capture complex relationships among items and users that traditional recommendation approaches may skip. For instance, embeddings can capture the semantic meaning of items or the contextual information of user behavior, allowing recommendation systems to make more accurate and relevant recommendations.

Embedding Techniques for Recommender Systems

Embedding techniques can help machines better understand natural language and perform more accurate NLP tasks. There are several different embedding techniques that can be used in recommender systems:

Matrix Factorization

Matrix factorization is a technique that decomposes the user-item interaction matrix into two lower-dimensional matrices representing users and items, respectively. This technique has been widely used in collaborative filtering approaches.

Neural Networks

Neural networks can be used to learn embeddings of items and users by training a multi-layer perceptron or a deep neural network on the interaction data. These embeddings can be learned through backpropagation and optimization techniques such as stochastic gradient descent. This approach is used in both collaborative filtering and content-based filtering approaches.


Autoencoders are neural networks that can learn compressed representations of input data. In recommender systems, autoencoders can be used to learn embeddings of items and users by encoding their features into a compressed representation and then decoding them back to their original form.

Graph Embedding

Graph embedding is a technique representing nodes in a graph (such as users and items) as points in a high-dimensional space, where similar nodes are mapped to nearby points. This technique has been used in recommendation systems that model the user-item interaction graph as a network.

Word Embedding

Word embedding is a technique that represents words as vectors in a high-dimensional space, where similar words are mapped to nearby points.

From Theory to Practice: Success Stories in Recommender Systems

Recommender systems have been successfully applied in various domains, including e-commerce, media and entertainment, social networking, and healthcare companies. Here are a few success stories of recommender systems in practice:


Amazon is one of the pioneers in using recommender systems in e-commerce. The company uses the embedding approach to give its clients customized product recommendations. This technique allows Amazon to represent each product and user as a vector of features that capture their preferences. 

Here’s how embedding assists in Amazon’s recommender system:

Amazon’s recommender system analyzes customers’ past behavior, such as browsing and purchasing history, to learn their preferences. Afterward, it uses embedding to create a low-dimensional representation of each customer’s preferences. This allows Amazon to recommend products that match the customer’s interests, even if they have never explicitly expressed interest in them.

Similarly, Amazon uses embedding to represent each product in its catalog as a low-dimensional vector of features. These features capture the product’s characteristics, such as its category, brand, price, and popularity. The embeddings also capture the relationships between different products, allowing Amazon to recommend products that are frequently bought or have similar attributes.

This has significantly improved the shopping experience for customers of Amazon and increased sales for the company. Considering that in 2022, according to Statista, Amazon’s mobile app was the most popular marketplace app in the United States, accumulating nearly 35 million downloads throughout the year.


Netflix uses a combination of recommender systems and embedding techniques to make personalized recommendations for its users. Here’s how it works:

To begin with collaborative filtering that Netflix uses to recommend its products based on what other users with similar viewing habits have watched. For example, if you’ve watched and enjoyed a series of action movies, Netflix’s algorithm will recommend other action movies that similar users have watched and enjoyed.

Netflix also uses content-based filtering to recommend movies and TV shows based on the attributes of the content itself. For instance, if you’ve watched a lot of romantic comedies, Netflix’s algorithm will recommend other romantic comedies based on common attributes like the lead actors, setting, and themes.

The platform uses embeddings to represent movies and TV shows in a high-dimensional space based on genre, director, actors, and plot attributes. These embeddings are learned through machine learning algorithms like neural networks. By using embeddings, Netflix can make recommendations based on similarities between the embeddings of content that a user has watched and those of content they haven’t watched yet, allowing them to create personalized recommendations.

According to Statista, in the fourth quarter of 2022, Netflix had nearly 231 million paid subscribers, which proves Netflix retains customers and increases the amount of time they spend on the platform.


YouTube uses various machine learning techniques, including recommender systems and embeddings. Its recommender system considers a wide range of factors, such as the video’s content, the video, the language of the video, the location of the user, and the time of day.

Meanwhile, embeddings represent the videos and users in a mathematical form suitable for machine learning. These embeddings capture the relationship between items and users and allow YouTube to predict which videos a user accurately will most likely watch next.

According to Statista, Youtube reported that in 2022, nearly 2.56 billion people used this platform worldwide.

Final Thoughts

The success stories above prove that embedding is a crucial technique in recommender systems as it helps to address several key challenges in the recommendation, such as accuracy, sparsity, scalability, and personalization, which leads to better user engagement and satisfaction. However, implementing embedding in a recommender system requires careful consideration of the appropriate method, hyperparameters, and evaluation metrics. 

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