Today’s generation is tech-savvy. They want everything at their fingertips, whether it’s shopping or watching movies. There are various social media, eCommerce, and streaming platforms that we all use daily. Have you ever wondered how these platforms suggest products, movies, or friends that match your interests so accurately? That’s where AI-enabled Recommendation Systems come into play.
Imagine having a helpful friend who knows your preferences and suggests things you’ll love. That’s what a recommendation system does in the digital world. It uses artificial intelligence (AI) to analyze your past actions, like what you’ve bought, watched, or liked, and then suggests new things you might enjoy. It’s like magic, but it’s actually science and data working together.
In this guide, we’ll take you behind the scenes to show you how to develop your AI-enabled Recommendation System and how it works. Let’s get started!
Market Stats of AI-enabled Recommendation Systems
Before, moving forward let’s get to know some market stats of AI-enabled recommendation systems.
- According to Straits Research, the global AI-enabled recommendation system market size is projected to reach USD 54 billion by 2030. Furthermore, it is expected to register a CAGR of 37% during the forecast period (2022 – 2030).
- Grand View Research stated that due to the increasing need to enhance customer satisfaction in the market of AI-enabled recommendation systems will continue to grow at a CAGR of 33.0% by the end of the year 2028.
- According to reports, Netflix states that algorithmically-generated recommendations influence 80% of its viewership.
What is an AI-enabled Recommendation System?
An AI-powered recommendation system is an algorithm prepared to provide you with suggestions based on your buying history, preferences, purchase patterns, ratings, etc. AI tools of eCommerce, social media, and streaming apps collect your data and analyze it to help you make more informed decisions.
An AI-enabled recommendation system is like a smart assistant that helps you everywhere, such as narrowing down your search list on an eCommerce app, providing you with a seamless binge-watching experience on a streaming app, connecting you with a person with similar interests on any social media platform, etc.
This system learns everything you do online and uses that data to understand your preferences. By understanding user preferences and identifying patterns in their interactions, these systems can provide users with product recommendations, movie suggestions, music playlists, or even social media posts that are more likely to resonate with their interests.
Let’s explore more about AI-enabled recommendation systems.
Step-by-Step Guide to Develop an AI-enabled Recommendation System
So that you have now learned what is an AI-enabled recommendation system, let’s move on to the next step. It’s the time you should learn how to develop an AI-enabled recommendation system. Here is a step-by-step guide to developing an AI-enabled recommendation system:
Step #1: Understand the Business
The first step to developing an effective AI-enabled recommendation system is to understand the business, its requirements, and how it works. You need to discuss the details with the data and business teams and ask for their valuable input. Here are some things that you should think and talk about to better understand business needs:
- What is the end goal of using a recommendation system?
- Can the company achieve its ultimate objective by promoting a static collection of information instead?
- At what point will recommendations occur?
- Do different parameters need distinct recommendation sets, or can they be the same?
- Which data are accessible, and on what suggestions are based?
- Should the product changes in the recommendation list on every search that must be made first?
- Should all content or products be treated equally, or teams can promote products aside from organic recommendations?
- How do you segment users with similar tastes?
These are crucial questions to answer before diving into building a recommendation system. It helps ensure everyone is on the same page and that the system will meet the business’s needs.
Step #2: Data Collection & Processing
A recommendation system requires a specific data set to suggest the appropriate item to the user. Hence, gathering various forms of data is a top priority. Gathering information on user behavior, item properties, contextual information, etc., is a part of this process.
Users’ interactions, third-party databases, and APIs are just a few examples of the various data sources. After gathering the necessary information, preprocess to accommodate missing values, eliminate noise, and normalize the data. This approach promotes the accuracy of the process to ensure the recommendation system doesn’t make mistakes.
Step #3: Select the Right Algorithm
There are several algorithms accessible, each with its advantages, disadvantages, and basic skills. You must make the appropriate choice depending on your industry and target market. The most widely utilized algorithms in recommendation systems across many platforms are the following:
- Matrix Factorization: The user-item interaction matrix is factorized using matrix operations into latent factors by this method, which then learns the most recent factor to provide the best advice.
- Neural Networks: With the help of this deep learning system, one can make accurate suggestions by learning intricate patterns from big datasets.
- Collaborative Filtering: To offer the appropriate products to another user based on their tastes and the product’s properties, this algorithm compares individuals and objects that are similar to one another. It then determines similarity ratings.
The data type, scalability needs, and business goals all influence the decision regarding the best algorithm to use.
Step #4: Model Training & Evolution
It is now time to train the model with previously processed information after choosing the appropriate algorithm. Here, data is fed into algorithms, the parameters of the model are changed, and the performance is assessed using appropriate metrics like precision, recall, and accuracy.
It is one of the most crucial processes since it helps the model’s accuracy and efficacy when providing the recommendation, making it one of the most crucial elements in the process.
Step #5: Implementing the Recommendation System
The final stage is to integrate the model into the recommendation system after completing the training process. The model must be integrated with the system’s backend or API to take user input, process data, and produce the appropriate recommendation in real time. As part of the stage, the recommendation system will also be customized based on user comments, contextual information, and preferences.
Types of AI-enabled Recommendation Systems
An AI-enabled recommendation system can be categorized into several types, including the following:
1. Content-Based Recommender System
A content-based recommendation system suggests stuff based on what you like. It looks at the details of the items, like articles or movies, and your past preferences. If you have enjoyed action movies before, it will recommend more action movies. It understands the depth of the product using keywords (or genre) and matches them with your tastes.
This way, it provides you with personalized recommendations, making it easier to find new things you’ll enjoy. This system doesn’t consider what others like; it’s all about your unique preferences, making it easier to discover things you’ll truly adore. It’s like having a virtual assistant that knows your preferences and suggests things you’ll love.
2. Collaborative Filtering
Collaborative filtering is a smart way that recommendation systems use to suggest things you might like, such as movies, products, or music. It works by comparing your preferences with those of other people. There are two main types:
- User-Based Collaborative Filtering: This type looks at what people similar to you have liked. If you and others have similar tastes, it suggests things those similar folks have enjoyed. It’s like asking your friends with similar interests for recommendations.
- Item-Based Collaborative Filtering: Instead of focusing on people, this type looks at similarities between the things you’ve liked and the things others have liked. If you’ve enjoyed similar items, it recommends those to you. Think of it like finding books at the library based on the books you’ve already read.
3. Hybrid Recommendation System
A hybrid recommendation system is like having a team of experts giving you suggestions. It combines different recommendation methods to provide more accurate and personalized advice.
For example, if you have a friend who knows your taste in movies really well, and another knows your favorite books. The hybrid system takes both friends’ insights and combines them to suggest movies and books that suit your unique preferences.
It’s like getting a double dose of personalized recommendations, making it easier to discover what you like. The hybrid system ensures you get a well-rounded mix of suggestions, enhancing your overall experience.
4. Knowledge-based Recommendation System
A knowledge-based recommendation system is like a wise advisor who suggests things based on your specific information. Instead of relying on your past choices or what others like, it considers your explicit preferences and requirements.
For instance, if you want a laptop with a large screen, lots of storage, and a specific brand, it finds options that match these criteria. It’s like asking a knowledgeable friend for advice, and they find products or content that align with your stated preferences. This type of system is great when you have clear requirements, as it offers recommendations based on what you’ve explicitly told it, helping you make informed choices.
How Does an AI-enabled Recommendation System Work?
AI-enabled Recommendation Systems are like smart assistants that help you discover things you might like, whether it’s movies, books, products, or friends on social media. These systems work in a fascinating way, and here’s how:
The system starts by gathering a lot of information about your activities and preferences while you’re using a website, app, or platform. It includes what you click on, buy, watch, or interact with. Think of it as collecting pieces of a puzzle to understand you better.
Once all this information is gathered, it’s safely stored in digital storage, like files neatly arranged in a cabinet. This storage ensures that the data is readily available for analysis whenever needed.
The real magic happens when the system’s computers and algorithms kick in. They carefully examine the stored data to understand your patterns and preferences. It’s like having a super detective who analyzes clues to understand your unique tastes and interests.
Equipped with this understanding, the system uses smart algorithms to sort through a vast array of options available. It matches your preferences with the available choices and recommends things it thinks you’ll like. It’s similar to a good friend suggesting a movie based on your favorite genre.
Benefits of Using AI-enabled Recommendation Systems
Now, let’s study about various benefits of employing an AI-enabled recommendation system.
1. Personalised Recommendation
An AI-based recommendation system collects users’ data and examines various factors, such as their buying history, search history, purchase pattern, and other information. It can provide them with a personalized recommendation list of products by analyzing all these users’ data that they might like to buy. It can provide personalized recommendations to many people simultaneously, increasing your sales.
2. Better Accuracy
Nowadays, it is easy for a user to search for their required product and order it in just a few taps. So, it is excessively important for search engines and eCommerce platforms to recommend the product with better accuracy. Brands that are likely to prove to provide accurate recommendations can easily attract and retain several customers. Hence, it will result in improved sales.
3. Context-Based Recommendations
Context-based recommendations are those where the system collects and analyzes data from the product’s description. For example, if you are searching for a movie on a streaming platform, it will pick the type of genre, artists, directors, and other relevant information. It will start processing this information and present you with a recommendation list of the same context. It will help increase the watch time of the user.
As these recommendation systems are AI-powered they can process a large amount of data in a short time. Thus, an AI-enabled recommendation system has no limit. It can provide various suggestions based on user’s input and their previous data. It can help provide a broader recommendation list to the user base.
5. Real-Time Recommendations
These days we witness the changing preferences of a customer. Their choices for buying products or watching some movies change every day. An AI-enabled recommendation system keeps itself updated with the ever-changing demands of the user. It repeatedly analyzes the user preferences and shows the results accordingly.
6. Improved User Engagement
One of the foremost benefits of an AI-enabled recommendation system for businesses is improved user engagement. With these systems, businesses can engage their users for a longer time on their platform which will increase their sales too.
7. Enhanced Business Revenue
As aforementioned, with an AI-enabled recommendation system, businesses can enhance their revenues. These systems help attract more customers, and with effective recommendations, people feel comfortable shopping from such platforms. Thus, it also helps generate higher revenue.
Top Companies Using AI-enabled Recommendation Systems
AI-enabled recommendation systems have become anonymously essential for various social media platforms, streaming apps, eCommerce platforms, and others. It helps a business enhance its user experience, personalize recommendations for each user, and increase sales and advertising revenues. Some of the popular companies that have employed AI-enabled recommendation systems are as follows:
Amazon, a world-class e-commerce brand is a great example of an AI-enabled recommendation system. There are a lot of options where Amazon has used AI for making recommendations. There are various options on Amazon that use AI recommendations, such as ‘Recently Viewed’, ‘Bought Together’, and ‘Recommended for You.’ These search results are based on the user’s search history, buying record, product rating, reviews, browsing behavior, purchasing behavior, and other valuable information. It has significantly improved the sales of Amazon by 29%.
Facebook uses a deep-learning and neural network-based recommendation engine named DLRM (Deep-learning Recommendations Model). It provides suggestions, sort the News Feed, recommend pages, groups, and product listing on Marketplace.
This DLRM model uses a smart algorithm to suggest content that users like. It analyses users’ past interactions, such as posts they have liked or people they have connected with, for similar recommendations. Sometimes, it also shows us similar posts or groups that our friends are engaging with.
YouTube uses AI-enabled recommendation systems to offer tailored video suggestions. These systems observe users’ viewing history, clicks, likes, dislikes, watch time, and shares to list the views that they may enjoy watching. It examines user’s preferences via the type of content they watch, video length, and their interactions to make smart suggestions. It increases users’ screen time and suggests more similar videos to make them use the app for a longer time. YouTube aims to refine its algorithm and give users more control over recommendations for a better viewing experience.
AI-enabled recommendation systems in LinkedIn help users enhance professional networking and job searching. These systems analyze your profile, including your skills, job history, and connections, to suggest relevant job opportunities that align with your career goals, like a career advisor who understands your ambitions.
However, there are concerns about potential biases in these recommendations, which LinkedIn is actively addressing to ensure fairness and inclusivity. Overall, LinkedIn’s AI recommendations aim to assist users in advancing their careers and expanding their professional networks.
Netflix employs AI-enabled recommendation systems to make users’ streaming experiences enjoyable. These systems examine user’s viewing history and preferences to suggest movies and TV shows they might like. They consider genres, actors, directors, and user ratings to provide tailored recommendations. It’s more like having a movie-lover friend who knows what to watch next. These recommendations help users discover new content easily. However, there’s a challenge in avoiding content bubbles and ensuring diversity in recommendations. Netflix continually fine-tunes its algorithms to balance user’s personalization and unique content, so that users can easily find their favorite and new data to watch.
Spotify leverages AI-enabled recommendation systems to elevate your music listening experience. The algorithm of recommendation systems dives into your listening history, examining your favorite songs and artists to propose new music that aligns with your taste. They consider elements like genre, tempo, and mood to curate playlists and radio stations tailored just for the user. These suggestions make it effortless for a user to discover the same genre of songs and artists. The recommendations are mainly determined by an AI system called ‘Bandits for Recommendations as Treatments’ (BaRT).
In the fast-paced business world, staying competitive is crucial. Recommendation systems offer a strategic advantage, aiding in achieving broader business objectives like boosting sales and user engagement. However, their success hinges on careful planning and adaptability.
Effective recommendation systems bring value without wasting resources, with ongoing maintenance as needed. To develop an effective AI-enabled recommendation system, you should hire a mobile app development company that has good experience. They will help you develop an AI-enabled recommendation system that generates accurate results for users and increases your revenue.