How to Build an AI Recommendation System: 2024 Guide for Businesses

Personal recommendations and curated experiences are in high demand among users. According to a McKinsey survey on customer experiences, 71% of customers expect tailored recommendations, while 76% become frustrated when they do not get it:

Why? Customers struggle with choice overload. They want the best deal, but processing all available options is beyond human capabilities:

Customers need help with buying decisions, and this is where AI development becomes the key driver of a company’s success. 

A custom AI-powered recommendation engine will suggest the most relevant options from millions available without paralyzing customer decisions. 

Here is an explanation guide for businesses. Let’s discuss below how AI recommendation systems work, how they process data, and what you need to build one. 

A recommendation system, a.k.a. recommender system, is a category of AI/ML-based programs that enable businesses to predict, refine, and find what users want from a continuously growing number of available options.

A recommendation system collects, analyzes, and processes Big Data, which includes a variety of user-related parameters, such as: 

The system identifies specific patterns and customer propensity and matches them with available offers.

A well-calibrated recommendation system lets users discover products or content they can barely find independently.

As a result, it enhances user engagement and experiences, leading to increased sales/in-app time, faster decisions, growing customer satisfaction, and retention. 

Businesses benefit, too. Based on the abovementioned McKinsey study, when used for personalization, AI recommendation engines can:

An AI recommendation system can suggest:

Every business involved in the consumer goods sector should implement a recommendation system. Here are a few examples:

AI recommendation engines leverage a mix of technologies and processes, which they use to:

The following steps are essential for accurate work of AI recommendation systems:

AI recommendation engines gather large volumes of heterogeneous data, which Internet users or specific customer groups generate. The more information they collect and process, the more reliable results they provide

The two main types present explicit and implicit data:

Explicit data includes expressed user opinions, such as product reviews, likes, or ratings. 

Implicit data relates to user behavior and interactions, including clicks, browsing activities, session length, or purchases.

At this step, recommendation systems analyze collected data on recurring patterns in user behavior.

Artificial Intelligence recommendation engines apply sophisticated algorithms and carefully weigh hundreds of parameters to train the model.

This lets them produce more accurate forecasts than traditional recommendation systems that use only dozens of data parameters.

AI-powered engines are also more flexible while looking for subtle similarities and distinctions in user interactions.

The recommendation system generates personalized suggestions based on the trained model, a user’s specific preferences, and past behavior, such as accepted or skipped suggestions, session time, added to favorites, and more. 

Correct data filtering and measuring the distance between similar items are important for personalized recommendations. Both let the engine predict which products or content suggestions users will likely find beneficial for them.

Self-learning is a critical step every high-end AI recommendation system should contain. The system evaluates every new user data, including activities, feedback, and direct interactions, which it processes to further align recommendations with user expectations.

The number of active users affects learning. When numerous users interact with the system, it gets more feedback and can produce accurate suggestions for different user needs. As a result, accuracy increases faster. 

Smart suggestion repurposing is equally important. Users can ignore a suggestion due to an incorrect estimate of its value, miss it because of an unfortunate placement, or prioritize other suggestions while lacking time or money.

An Artificial Intelligence recommendation engine should include a balanced strategy on when and where the system should reshow a specific suggestion. The strategy must ensure users receive relevant recommendations but are not annoyed by too-frequent reshows.

The success of every AI recommendation engine depends on the accuracy of applied algorithms. Those can be very different because businesses need to customize and tune the applied algorithm based on specific goals and available information.

Still, the entire variety of AI algorithms and approaches falls into several broad categories:

Let’s discuss how they work.

Collaborative filtering is the most popular algorithm in recommendation engines. 

Collaborative algorithms make suggestions based on user preferences and previous interactions of other users with specific items. The concept behind this algorithm is that when a user with similar tastes, habits, or preferences chooses an item, another user will likely prefer the same selection. 

The two categories include:

Pros

Cons

A content-based system recommends items similar to those the user has already interacted with.

The system extracts relevant data from item descriptions and matches it with the user profile. The latter contains preferences based on previous consumption records, what the user previously liked and disliked.

The system promotes the best-matched items and penalizes those users who could dislike them.

Pros

Cons

Knowledge-based systems are based on explicit knowledge about items and user preferences. The system collects and extracts knowledge from previous cases, which it uses as a reference when adapting a recommendation to a new case. 

This type of recommendation engine works best for industries relying on specific domain expertise, such as legal, healthcare, real estate, or finance. 

Pros

Cons

A hybrid recommendation engine integrates several approaches within one comprehensive solution. It aims to overcome the limitations of single techniques and improve the accuracy of provided results. 

The hybrid approach works best: most currently implemented recommendation systems are hybrid. 

Pros

Cons

Deep-learning recommender systems use non-linear neural architecture to effectively handle diverse data formats and multiple input variables.

They enable businesses to capture non-trivial relationships between items and user preferences, improving recommendation quality. 

More importantly, deep-learning systems can recognize texts, images, and speech, extending search beyond keywords and making various search types possible, e.g., ask a voice assistant to find that YouTube channel where Gordon Ramsey eats hot chicken wings. 

Pros

Cons

A brand new technology, Generative AI, can revolutionize customer interactions with recommendation systems. Generative Artificial Intelligence recommendation engines can analyze user data and fill in gaps, providing robust recommendations. 

They are also more effective in detecting hidden patterns compared to “traditional” recommendation engines, improving recommendation accuracy. 

Pros

Cons

Machine Learning (ML) is a critical part of today’s recommendation engines. Here is why: 

Just as customers can be overwhelmed by the number of available offerings, finding correlations inside immense data volumes required to build and refine recommendations is beyond engineers’ comprehension capabilities. 

ML techniques for data processing leverage large datasets containing historical data, user behavior, and real-time activities for predicting user interest in specific items. Businesses receive meaningful insights, which they can use to improve digital experiences.

More importantly, recommendation systems need Machine Learning to improve results automatically based on newly acquired data. 

AI developers initially train ML-powered recommendation systems on a limited data set and under strict supervision. This is required to evade the cold-start problem and ensure a correct response.

When the system produces relevant and meaningful results, the team moves to semi-supervised and unsupervised learning steps, where the engine processes large data volumes and receives less strict control over results. Engineers let the system collect and analyze user feedback, which they incorporate into system improvements.

The next step is reinforcement learning. Engineers move to this step when the system is ready to ensure continuously growing accuracy in an autonomous mode. ML algorithms are capable of identifying valuable patterns and adapting recommendations accordingly.

AI recommendation engines enable businesses to create a different market position and make stable growth possible. 

Businesses integrating an advanced AI recommendation system can serve thousands or even billions of users—when talking about YouTube or Amazon—without compromising quality. They can improve services by extracting more insights from skyrocketing user data volumes. 

Here is what AI provides:

Decision time

Search is simplified, while AI tops relevant suggestions. Customers spend less time and effort looking for what they want. They are more likely to make a decision and have fewer reasons to continue their search using competing services. 

Discoverability

With a fine-tuned AI recommendation engine, customers can discover specialized product lines or solutions that are not popular but match their needs best. Also, AI can suggest supplementary items that customers want to buy but cannot find on their own. 

Customer satisfaction

Customer satisfaction grows as customers save time and receive personalized services tailored to their needs. Due to decreased choice fatigue, they will spend more time interacting with your offers.

Customer retention

AI increases the probability of turning visitors into a loyal customer base. Trust is built when customers understand the service meets their needs and preferences, which in turn positively affects the number of returned visits.

Conversion rates

AI recommendation engines drive sales: 35% of purchases on Amazon and 70% of those on Netflix are generated through AI recommendations. Among other things, AI helps qualify prospects and display a deal at the right time, motivating visitors to buy. 

Cross- and upselling

Knowing the service provides useful suggestions, customers are more willing to buy an extra item, a complementary product, an upgrade, or a premium account. 

Improved brand image

AI improves a platform’s user-friendliness and helps remove entry barriers for not-tech-savvy customers. Those will recommend a business to their friends and colleagues and leave positive feedback on review platforms. 

Operations scalability

Last but not least, AI streamlines operations and increases productivity. It lets businesses provide recommendations to any number of future or current customers without growing exponentially sales and support departments.

Now, let’s consider a six-step process for building an AI recommendation engine: 

Business owners should determine their goals, expectations, and success criteria from the future AI recommendation system, which they discuss with the development team. Goals can be:

Also, it is necessary to determine:

The team turns the input information into a set of system requirements, which it uses to prepare a detailed project estimate and a development plan. The team addresses such crucial points as project budget and timeline, team composition, software architecture, required third-party integrations, and acceptance criteria. 

Plan elaboration continues until project details are clarified and an agreement is reached.

The team works on collecting and preparing data for the AI recommendation system. This step includes:

Also, the team must design a relevant database architecture if it was not part of the planning step.

Collected data, such as customer information and reviews, require different types of databases for the best storage and retrieval. When the team leaves the question unanswered, the system will likely be slow, cumbersome, and non-scalable.

Scope of work can be significant at this step, which is needed to ensure AI will use relevant data for training and identify correct patterns in user behavior. 

Depending on the system type, AI engineers select one or several suited algorithms. They select an AI model and train it using the prepared data. 

The quality of the implemented model is important for getting relevant outcomes. Still, tradeoffs are unavoidable.

The team must look for the best balance between software performance, model complexity, accuracy, and required computational resources.

Otherwise, the recommendation system can easily become resource-consuming, which results in unreasonable maintenance costs and low ROI.  

Model development requires several iterations until the team finds the right combination of weight and bias, performance, and operational costs.

When the AI recommendation system is ready and provides forecasts with sufficient fidelity on the training data sets, the team starts testing it on unseen data. Engineers evaluate the accuracy of received predictions; they tune and refine the specific model parameters. 

At this step, the team can also start beta testing on real users to see how they use the system and what results they will get. It collects beta users’ feedback, makes an analysis, carves out insights, and uses them to introduce changes. 

The team can even return to the data preparation step when the accuracy of the new results is below expected, or adding more data will improve suggestions. 

When both the business and the team are satisfied with the output, the team switches to the production-ready stage.

After the main work on the AI recommendation system is complete, it is tested and stabilized, and relevant results are provided. Then, AI engineers deploy it on live servers.

Recommendation functionality becomes available to a wide audience from the business’s website or app or as a stand-alone solution. 

Some flaws can only be visible in the live environment. Here is why the development team continues to monitor the system behavior to ensure system availability. It also responds to user feedback with hotfixes and updates, crucial for uninterrupted experiences. 

After it is clear that the AI recommendation engine works as expected, the team moves to the next step: updates and maintenance.

Every AI recommendation system requires regular updates, constant security monitoring, and infrastructure optimization. The team tunes resource usage, instantly adding extra resources when user load spikes. Unnecessary resources are switched off after the load decreases, allowing businesses to save large software budgets. 

It helps update the model, add and adjust different parameters, and roll back when a new update does not work as expected. 

The monitoring team also tracks infrastructure and software integrity to eliminate vulnerabilities. Due to the amount and nature of the data they process, AI recommendation engines are valuable targets for cybercriminals.

Implementing the right AI recommendation engine can be daunting. Concerns remain that investment will be made into the wrong technologies or that implementation will generate less value than initially expected. 

Businesses should start with a careful evaluation of available options and related risks:

A custom AI recommendation engine best meets unique business needs. It ensures optimum flexibility and data safety, but training and deployment take time.

An off-the-shelf solution can be added fast. However, business depends on a provider’s product development and use policies.

Estimating the cost structure of the future solution is crucial, and this task can be complex. For instance, Google charges its customers per node per hour for model training and per search and browsing queries for prediction requests. 

In the case of a custom solution, businesses will need to pay for cloud time and understand what cloud services they will use and how to ensure wise spending.

Development costs are high for a custom system: start with a discovery. Outcomes of the discovery phase will help understand how costs can be lowered without compromising solution quality.

An off-the-shelf solution will require integration and tuning work, too—consider hiring AI consultants to optimize expenses.

A great recommendation system is complex. It operates hundreds of parameters in real-time while combining several algorithms for successful operation. But at the same time, it requires much computational power. 

When the budget is limited, it is wise to focus on value and include those algorithms and parameters that bring the best results for the least investment and schedule enhancements for the future.

It is important to decide in advance who will maintain the recommendation engine after its deployment. You need a DevOps team to escape the scenarios when system quality degrades, it slows down, or the infrastructure is not optimized.  

Responsive support is often the decisive factor for or against a specific off-the-shelf solution. Still, be prepared that premium, fast support will be included in expensive pricing packages.

Security of sensitive user data is the top priority. Your AI recommendation engine must conform to international and local regulations on collecting, storing, and processing user data. 

Before you opt for an off-the-shelf system, clarify how the vendor stores and depersonalizes customer data and whether it transfers any data to undisclosed third parties. 

Here is what makes IT Craft a go-to partner for implementing AI recommendation systems:

IT Craft always invests in new directions and competencies. At the same time, we also work on improving business analysis and project management skills to meet changing business goals and constraints. Our engineers focus on AI technologies that match your vision and budget considerations.

Our AI developers can help you at every step of your project:

Our responsibility is to find and add the tech expertise you need for success.

IT Craft has been operating in the market for over 20 years, serving across 20+ industries. Our teams have acquired extensive expertise in different software development domains, which will help you exceed your customers’ expectations.

Prioritizing an Artificial Intelligence recommendation engine is crucial for meeting customer needs and staying competitive in a rapidly disrupting business environment. 

Expertise and agility are important for a smooth system. You will need to go through a series of iterations to design, train, calibrate, and launch an accurate recommendation solution. After that, a team will be required for updates and maintenance. 

AI implementation can be difficult. However, AI recommendation engines are a valuable investment, despite complexity.

With AI’s help, you can:

Everything needed to stay on top of the competition.