🎉 AI Engineers: Join our free webinar on how to improve your RAG performance. Secure your spot >

May 22, 2024 - last updated
Real-world Applications and Use Cases

What are recommender systems? Use cases, types, and techniques

Maor Michael
Maor Michael
13 min read Feb 27, 2023

A recommender system, also known as a recommendation system, is a subclass of information filtering systems that seeks to predict the “rating” or “preference” a user would give to an item. 

Recommender systems are used in playlist generators for video and music services, product recommenders for online stores, content recommenders for social media platforms, and in many other areas. These systems operate by gathering data on users’ past behavior, such as purchasing history or watched videos, and use that information to calculate a prediction of what they may have an interest in in the future. 

There are several algorithms that can be used to build recommender systems, such as collaborative filtering, content-based filtering, and hybrid systems that combine both methods. The goal of a recommender system is to personalize the user experience by providing highly relevant and useful recommendations.

This is part of an extensive series of guides about machine learning.

Recommendation Systems: Benefits and Use Cases 

There are several key benefits of recommendation systems, including:

  • Personalization: Recommendation systems provide personalized recommendations to users, based on their past behavior, preferences, and interactions with the system. This allows users to receive recommendations that are tailored to their individual needs and interests.
  • Increased engagement: Recommendation systems can help increase engagement with a system by providing users with content, products, or services that are likely to be of interest to them. This can help to keep users engaged and active, and can also lead to increased sales or conversions.
  • Improved user experience: Recommendation systems can improve the overall user experience by making it easier for users to find the content, products, or services that they are looking for. This can help to reduce frustration and increase user satisfaction.
  • Increased revenue: Recommendation systems can help businesses to increase revenue by providing personalized recommendations that lead to increased sales and conversions. By providing users with products or services that are more likely to be of interest to them, businesses can improve the chances of making a sale.
  • Better understanding of user behaviors: Recommendation systems can help businesses to gain a better understanding of user behaviors and preferences. This information can be used to inform marketing strategies, product development, and other business decisions.

Recommendation systems have a wide range of use cases across several industries, including:

  • eCommerce: Recommendation systems are widely used in eCommerce to provide personalized product recommendations to customers based on their past behaviors and preferences. Learn more about recommendation systems in eCommerce (coming soon)
  • Entertainment: Recommendation systems are used in entertainment, such as music and video streaming services, to recommend content that is likely to be of interest to users.
  • News and media: Recommendation systems are used in news and media platforms to recommend articles, videos, and other content that is relevant to a user’s interests.
  • Social media: Recommendation systems are used in social media to recommend friends, groups, or posts that are likely to be of interest to users.
  • Healthcare: Recommendation systems are used in healthcare to provide personalized recommendations for treatments, medications, and other healthcare services.
  • Finance: Recommendation systems are used in finance to provide personalized recommendations for investments, credit products, and other financial services.
  • Advertising: Recommendation systems are used in advertising to provide personalized recommendations for ads and offers that are likely to be of interest to users.
  • Education: Recommendation systems are used in education to provide personalized recommendations for courses, programs, and other educational content.

Learn more in the detailed guide to content based recommender systems (coming soon)

The Role of Machine Learning in Recommendation Systems 

While most people can understand the basic function of a recommender system, the underlying ML mechanism that enables the predictions is more complex. Machine learning algorithms can be used to analyze data on user behavior and preferences, and to identify patterns in that data that can be used to make recommendations. This data can include user ratings, product views, search queries, and purchase history, among other things, which help the algorithm predict the products or items that a user is most likely to be interested in.

As a user interacts with the recommendation system and provides additional data, the algorithms can continuously learn and improve their predictions. The use of machine learning in recommendation systems results in more accurate and relevant recommendations, resulting in a better user experience and higher engagement, as users are presented with recommendations that are tailored to their individual preferences and interests.

Learn more in the detailed guides to:

  • Recommender systems in machine learning (coming soon)
  • Recommender systems in deep learning (coming soon)

Types Of Recommendation Systems 

There are several types of machine learning algorithms that are commonly used in recommendation systems:

Collaborative Filtering

Collaborative filtering is a technique used in recommendation systems to make predictions about an individual’s preferences based on the preferences of similar users. The idea behind collaborative filtering is that people who have similar preferences in the past are likely to have similar preferences in the future.

Collaborative filtering algorithms use data on the interactions of a large number of users with a particular item, such as their ratings or purchasing history, to identify similar users and make recommendations. The algorithms calculate similarity scores between users, and use these scores to predict what items a target user is likely to be interested in.

There are two main approaches to collaborative filtering: 

  • User-based collaborative filtering: In this approach, the algorithm provides recommended items that were preferred by users with similar preferences to the target user. For example, in an eCommerce store, if similar users wanted low cost items, the algorithm will suggest showing similar low-cost items to the current user.
  • Item-based collaborative filtering: In this approach, the algorithm finds similar items to the ones that the target user has liked, and recommends those items to the user. For example, if the user previously purchased clothing for children aged 10, the algorithm can suggest more items suitable for children of that age.

Content-Based Filtering

Content-based filtering is a technique used to make predictions about a user’s preferences based on the characteristics of the items that the user has liked in the past. This approach focuses on the attributes of the items being recommended, such as genre, director, actor, or other features. It can be applied to any type of item that has explicit or implicit attributes, including books, movies, music, and products.

The idea behind content-based filtering is that if a user has liked items with certain attributes in the past, they are likely to like items with similar attributes in the future. The algorithm uses the user’s past behavior, such as ratings or purchase history, to build a profile of the user’s preferences. This profile is then used to recommend items that have similar attributes to the items that the user has liked in the past.

Hybrid Systems

Hybrid recommendation systems use multiple recommendation techniques to provide more accurate and diverse recommendations. They combine the strengths of different recommendation approaches, such as collaborative filtering and content-based filtering, to provide a more comprehensive and personalized recommendation experience.

A classic example of a hybrid recommendation system is Netflix, which uses a combination of collaborative filtering, content-based filtering, and other techniques to make recommendations. Netflix uses collaborative filtering to understand the preferences of similar users, and content-based filtering to understand the properties of the items that a user has liked in the past. By combining these two approaches, Netflix is able to provide users with recommendations that are both relevant and diverse.

Hybrid recommendation systems can also incorporate other sources of information, such as demographic data, contextual data, or external data sources, to make more accurate recommendations. 

Recommender Systems Algorithms and Techniques 

Matrix Factorization 

Matrix factorization techniques are used in recommendation systems to analyze the relationship between users and items. The goal of matrix factorization is to factorize a large user-item matrix into a smaller set of latent representations, or “factors,” that capture the underlying relationships between users and items.

Matrix factorization algorithms can be used to make predictions about user preferences by mapping users and items to these latent factors and using the relationships between the factors to make recommendations. There are several types of matrix factorization algorithms, including singular value decomposition (SVD) and non-negative matrix factorization (NNMF).

Deep Neural Networks

Deep neural networks are a type of machine learning algorithm that are inspired by the structure and function of the human brain. They consist of multiple layers of interconnected nodes, or “neurons,” which process information and make predictions.

Deep neural networks can be used in recommender models to analyze large amounts of data and make more accurate and personalized recommendations. They can be trained on user-item interactions, such as ratings or purchase history, to learn the complex relationships between users and items and make predictions about user preferences.

Two types of deep neural network architectures commonly used in recommender models is the autoencoder, which is trained to learn a compact representation of the user-item interaction data, and generative adversarial networks (GAN), which can help solve the data noise and data sparsity issues in recommendation systems.

Contextual Sequence Learning

Contextual sequence learning is a type of machine learning algorithm that considers the context and sequence of interactions in ongoing user interactions or time series data, when making recommendations. For example, it can understand the underlying patterns and relationships between the items a user has interacted with in a browsing session, and use that information to make recommendations based on the current context of the session.

In traditional recommendation systems, recommendations are usually made based on the overall history of a user, and do not explicitly model the context of the current interaction in the sequence of previous interactions. However, the context in a sequence of interactions can provide valuable information about a user’s current interests and preferences.

Contextual sequence learning algorithms can be used to model the sequential relationships between items, such as the order in which items were interacted with, the time between interactions, and the duration of the session. This information can be used to make more accurate recommendations based on the user’s current context. 

Wide and Deep

Wide and deep networks are a type of deep learning algorithm that combines the strengths of both wide and deep neural networks. A ‘wide network’ is designed to learn simple, local relationships between features, while a ‘deep network’ is designed to learn complex, global relationships between features.

In recommendation tasks, wide and deep networks can be used to capture both the memorization of specific user-item interactions, as well as the generalization of patterns and relationships between users and items. The wide component can learn simple, local relationships between user and item features, such as the frequency of interactions or the average rating for an item, while the deep component can learn complex, global relationships between user and item features, such as the relationship between the user’s age and the items they prefer.

Learn more in our detailed guide to recommender systems algorithms (coming soon)

How to Evaluate the Performance of Recommender Systems 

Evaluating the performance of a recommender system is an important step in the development and deployment of a recommendation system. The evaluation process helps to assess the quality and effectiveness of the recommendations generated by the system and to identify areas for improvement. Some common evaluation metrics for recommender systems include:

  • Accuracy: This metric measures the accuracy of the recommendations generated by the system. It can be calculated as the number of correct recommendations divided by the total number of recommendations.
  • Recall: This metric measures the ability of the system to find all relevant items for a user. It is calculated as the number of relevant items found divided by the total number of relevant items in the dataset.
  • Precision: This metric measures the proportion of relevant items among the items recommended by the system. It is calculated as the number of relevant items found divided by the number of items recommended by the system.
  • F1 score: This metric is the harmonic mean of precision and recall and provides a single, combined score that takes into account both the accuracy and completeness of the recommendations.
  • Mean Average Precision (MAP): This metric measures the average precision of the recommendations across all users. It is calculated as the mean of the average precision scores for each user.
  • Normalized Discounted Cumulative Gain (NDCG): This metric measures the ranking of the recommended items for a user. It is calculated as the sum of the discounted gain for each recommended item, normalized by the ideal discounted gain.
  • Receiver Operating Characteristic (ROC) curve: This is a graphical representation of the true positive rate (TPR) against the false positive rate (FPR) of a recommendation system, which can help to visualize the performance of the system.

Recommender Systems with Aporia

Aporia is the leading ML observability platform, trusted by Fortune 500 companies and industry leaders to visualize, monitor, explain, and improve recommender systems in production. Data scientists using Aporia can detect and mitigate issues such as recommendation bias, model drift, and cold start problems, ensuring the system is operating at peak efficiency. By monitoring these key metrics, ML stakeholders can quickly identify areas for improvement and fine-tune the models to deliver the best possible recommendations to end users, resulting in higher customer satisfaction and increased revenue.

The Aporia platform fits naturally into your existing ML stack and seamlessly integrates with your existing ML infrastructure in minutes. We empower organizations with key features and tools to ensure high model performance: 

Visibility

  • Single pane of glass visibility into all production models. Custom dashboards that can be understood and accessed by all relevant stakeholders.
  • Track model performance and health in one place. 
  • A centralized hub for all your models in production.
  • Custom metrics and widgets to ensure you’re getting the insights that matter to you.

ML Monitoring

  • Start monitoring in minutes.
  • Instant alerts and advanced workflows trigger. 
  • Custom monitors to detect data drift, model degradation, performance, etc.
  • Track relevant custom metrics to ensure your model is drift-free and performance is driving value. 
  • Choose from our automated monitors or get hands-on with our code-based monitor options. 

Explainable AI

  • Get human readable insight into your model predictions. 
  • Simulate ‘What if?’ situations. Play with different features and find how they impact predictions.
  • Gain valuable insights to optimize model performance.
  • Communicate predictions to relevant stakeholders and customers.

Root Cause Investigation

  • Slice and dice model performance, data segments, data stats, or distribution.
  • Identify and debug issues.
  • Explore and understand connections in your data.

To get a hands-on feel for Aporia’s advanced model monitoring and deep visualization tools, we recommend:

Book a demo to get a guided tour of Aporia’s ML observability and understand how we can help you achieve your ML goals.

 

See Additional Guides on Key Machine Learning Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of machine learning.

Auto Image Crop

Authored by Cloudinary

Advanced Threat Protection

Authored by Cynet

Multi GPU

Authored by Run.AI

Green Background

Control All your GenAI Apps in minutes