A Comprehensive Guide to Building an AI-Powered Recommendation System

Artificial Intelligence is reshaping how businesses operate, perform various tasks, and interact with their target audience. Chatbot is a small example of AI implementation in the online business landscape where customers can solve their problems in no time, even without human chat support. According to Grandviewresearch, the global artificial intelligence market size was valued at USD 136.55 billion in 2022, expected to show a CAGR of 37.3% from 2023 to 2030. 

One of the most popular and best use cases of artificial intelligence is the AI power recommendation system that helps customers find the products of their interest. Today, we will talk everything about this use case of artificial intelligence and how it is changing customer interaction with E-commerce.

What are AI-Powered Recommendation Systems?

An AI-based recommendation system is a tool that understands consumer behavior and suggests relevant products and services to them. Offering the products and services that customers need increases the chances of sales. While searching for a pair of shoes on Amazon, you must have observed the advertisement of the same product all over your social media accounts, search engines, and third-party applications. All this synchronization takes place because of the recommendation system. 

Importance of Recommendation system 

• Already, 35% of consumer purchase on Amazon, and more than half of the movies that users watch comes from the recommendation system. So, there is no doubt that this AI system does a lot of work. 
• A recommendation system enhances the user’s likelihood of buying the recommended item. 
• It helps to reduce the transaction cost of finding and selecting the item in online shopping. 
• It betters the decision-making process and quality by suggesting the relevant products. 
• It helps increase the firm’s revenue by increasing the chances of sales.

How does an AI-based Recommendation System work?

Any artificial intelligence system works on particular algorithms that help it understand the subject behavior and perform the actions accordingly. Similar is the case with the recommendation system, where it learns about consumer behavior, past purchases, products in the wishlist, search intent, and other demographic details to suggest the right things to them. When all this information is considered while suggesting the product, the chances of getting the deal increase significantly. Any recommendation system mainly processes the data in the following four phases- 

1. Collection 

First of all, the data is collected. Here, the data can be explicit (includes ratings and comments on products) as well as implicit (includes order history, page reviews, etc.). 

2. Storing 

Here, the type of storage is decided based on the type of data used to create the recommendation system. The options are NoSQL database, object storage, standard SQL database, etc. 

3. Analyzing 

After storing the data, the recommendation system finds a product with similar user engagement data after sufficient analysis. 

4. Filtering 

Here, the game of algorithm begins where all the data gets filtered to access the relevant information. This information is then used to provide the proper recommendations to the users. 

Types of AI-Powered Recommendation Systems 

While we have witnessed the use of recommendation systems in different landscapes, it is not possible for a single tool working on a single algorithm to provide different results and experiences. Therefore, different types of AI-powered recommendation systems utilize numerous algorithms to show the results. Here are some of the most used AI-powered recommendation systems that are used in popular platforms- 

1. Content-based recommender system

Content-based recommender system mainly entertains the feature of a product, and it learns the profile of a new user’s interest based on association with the particular feature of that object. For example, if the user has made a purchase in the past and the recommendation system finds another product with the same quality, it will suggest this product to the user.

2. Collaborative filtering 

Collaborative filtering is completely based on analyzing and gathering data on the user’s behavior. Interpreting in simpler words, it tracks the user’s online activities and predicts what they might like based on the similarities with other users. For example, if person 1 likes A, B, and C while person 2 likes A, B, and D, it is predicted that person 1 will also like D. Similarly, person 2 will most probably like C. 

3. Hybrid Recommendation systems 

When both content-based and collaborative filtering is used to recommend products,  it is called a hybrid recommendation system.  The best example to define this type of recommendation system. It suggests the movies and series by analyzing users’ watching and searching habits and then finding other similar users.

4. Knowledge-based Recommendation system 

Talking about the knowledge-based recommendation system directly interacts with the need of a particular user and how a particular product can meet and fulfill that needs. Here, the recommender system has certain knowledge about the customer’s need and thus automatically enhances the chances of making a deal with the customer.

5. Non-personalized Recommendation System 

While all the other types of recommendation systems are engaged with some type of algorithm, the non-personalized recommendation system is far away from it. The system works based on the popularity of products. So, no matter if it is relevant to the need of customers or not, as long as the product is popular in the category, it will be recommended to the target user. 

Benefits of using AI-powered Recommendation Systems 

While we have already discussed the core competencies of a recommender system in a business and how it instills a change in the operational process, there is a range of benefits that a recommendation system delivers to the business owner. Let’s have a glance over some of them- 

1. Personalized Recommendations 

An AI-based recommendation system collects and analyses a range of data about the user, their buying habits, interest, and purchases. So, it can provide personalized recommendations to millions and billion of users simultaneously and thus increase the chances of sales to a significant extent. 

2. Better accuracy 

The algorithms of AI, such as matrix factorization and deep learning, help analyze the complex patterns of the data. The practice helps the system discover the products that align with the user’s interests and needs and provide accurate recommendations. So, the conversion rates and customer loyalty increase automatically. 

3. Context-aware Recommendations 

AI-powered recommendation systems are much more than you think. It can easily track contextual data, including location, device, time, and much more, to generate more relevant results. For example, tracking a user’s location and time of the day can easily suggest some good restaurants to the user.

4. Scalability

There is no limit on the number of users and data size, making the recommendation system a scalable solution. Analyzing the data of a vast target audience can simultaneously suggest the right product and service to a broader user base. So, a business can easily establish stronger relationships with its customer base.

5. Real-time Recommendations 

The preferences of customers change every day. So, as long as the AI-powered recommendation system is taking the command, the changing trends can not negatively impact the business. The recommendation system tracks the latest data of the users and suggests the right products according to their latest preferences and interests.

6. Improved user engagement 

User engagement with the brand and business can be fostered easily by providing personalized recommendations about the product and fulfilling customers’ needs. Enhanced user engagement instills a stronger relationship between the customers and the brand, ensuring long-term profit for the firm.

7. Enhanced business revenue 

As interpreted earlier in the section, businesses’ revenue has multiplied with the implication of recommendation systems in their operations. An increase in conversion and cross-selling opportunities significantly boosts business sales and leads automatically to higher profitability. 

How to Build an AI-Powered Recommendation System

Recommendation systems do wonders for business owners by increasing revenue and bringing in more customers. At the same time, building the recommendation system is not as easy as it might look at first sight. Many algorithms and technicalities must be fostered to make the system work. However, following the right development tactics can help ensure the best result. The below steps can be helpful in developing an efficient recommendation system-

1. Data collection and preprocessing 

A recommendation system needs a certain set of data to recommend the correct item to the right user. So, the foremost need is collecting the different types of data. The step includes gathering user behavior data (browsing history, ratings, purchase history), item attributes (keywords, genre, metadata), contextual data (time, device, location), etc. There can be several sources of data, such as user interaction, external databases, and APIs. 

Having collected the relevant data, it is preprocessed to remove the noise, normalize it, and handle missing values. This practice fosters accuracy in the process so that the recommendation system can avoid making mistakes.

2. Select the right algorithms 

There are numerous algorithms available with their strengths, weaknesses, and core competencies. You have to select the right one based on your business and target audience. The following are the most popular algorithm being used in recommendation systems of different platforms- 

• Matrix factorization- this algorithm uses matrix operations to factorize the user-item interaction matrix into the latent factors and learns the latest factor to make the right recommendations. 
• Neural networks- this is a deep learning algorithm that can be used to learn complex patterns from large datasets and thus helps in making the right recommendations. 
• Collaborative filtering- this algorithm finds similar users and items to recommend the right products to another user with similar interests. 
• Content-based filtering- this algorithm uses the item attributes and calculates the similarity scores to make the recommendation based on user preferences and item characteristics. 

The decisions about the choice of right algorithm depend on the nature of data, scalability  requirements, and business objectives.

3. Model training and evaluation 

When the right algorithm has been selected, it is time to train the model with preprocessed data. This step includes feeding data into algorithms, adjusting the model’s parameters, and evaluating the performance using suitable metrics like precision, recall, and accuracy. 

It is one of the most important steps in the process as it assists in model’s accuracy and effectiveness while making the recommendation.

4. Implementing the recommendation system 

Once the model has been trained, implementing it into the recommendation system is the last step. It includes integrating the model into the system’s backend or API to allow it to receive user input, process data, and thus generate the relevant recommendation in real-time. The step also includes the practices like customizing the recommendation system based on user feedback, contextual data, and preferences.

Top Use Cases of AI-Based Recommendation Systems

The recommendation system is one of the most advanced applications of artificial intelligence, consuming data and giving output all by itself. In such a scenario, no industry is lagging to leverage the benefit of this AI tool. Today, AI-based recommendations fulfill a range of use cases in different landscapes. The followings are some of them-

1. E-commerce 

As you can observe, from the beginning of the blog, most of our example about recommendation system implementation belongs to E-commerce. The reason is, E-commerce is one of the biggest sectors largely dependent on the use of this AI tool. By analyzing the user’s browsing history, product rating, purchase history, reviews, purchasing behavior, browsing history, and other relevant information, this tool can suggest the right product to the user and increase user engagement and conversion rates.

2. Content Recommendation 

Other than E-commerce, it is highly used in recommending entertainment content to the user. For example, the video streaming platforms like Amazon Prime and Netflix use the recommendation system to suggest movies according to the taste of the users. It is instrumental in recommending TV shows, articles, news, music, and other content as well.

3. Social Media

A recommendation system in social media helps to suggest friends, groups, pages, and content to the users on the basis of their social connections, interests, and engagement history. 

4. Online Advertising  

Providing personalized advertisements to users on different platforms on the basis of their browsing behavior, interests, and demographics is also a popular use case of AI-based recommendation systems. This practice helps to deliver the advertisement to targeted users with increased relevancy. 

5. Travel and Hospitality 

Businesses operating in the travel and hospitality industries recommend hotels, flights, restaurants, and other activities to users based on their travel history, reviews, and preferences.

Personal Finance 

The AI-based tool even provides recommendations in finance terms by tracking the user’s income, expenses, investment goals, and savings. Thus, users can better manage their finances, make better financial decisions, and achieve their financial goals.

6. Healthcare 

Tracking the user’s health history, symptoms, and genetic information and recommending personalized treatment plans, medical recommendations, and health monitoring suggestions has been a significant use case of AI recommendation tools.

7. Human Resource 

By analyzing the data such as qualification, employment history, performance, and job application of a person, the recommendation system helps to suggest similar jobs and opportunities with relevant skills and qualifications requirements.

8. Food and Beverage 

Recommending recipes, meal plans, and dining options to users on the basis of their dietary preferences, allergies, and cooking skills has become one of the most popular use cases of AI-based recommendation systems.

9. Online Dating 

Online dating platforms are booming significantly among youngsters as they can easily find the perfect partner on this software. Recommendation systems track the interests, preferences, and past interactions of the user and then help to recommend compatible partners in no time.

Challenges to consider while developing AI-based Recommendation Systems

While recommendation systems deliver a range of benefits to online business owners, certain challenges act as a hurdle in leveraging these benefits. The challenges are but are not limited to-

1. Data quality and availability 

Recommendation systems are all about the data they have to make decisions. So, lack of data or the poor quality of data might impact the accuracy of the recommendation system. Ensuring data accuracy, addressing data biases, and dealing with sparse or incomplete data are big challenges.

2. Privacy and security concerns 

Recommendation systems mostly need sensitive user data, which includes browsing history, personal preferences, purchase history, etc., to suggest the right products. So, ensuring the security and privacy of this data becomes challenging. 

3. Cold start problem 

A cold start problem is one when a new user enters the platform, which doesn’t have much past data. In such cases, the recommendation system has no grounds to suggest the products. It is a big challenge for this kind of AI tool.

4. Scalability and real-time processing 

Mostly, the recommendation systems must address a broad range of audiences and traffic, analyze the data and provide a suitable recommendation. So, scalability and real-time processing is often a big issue.

5. Diversity and fairness 

Avoiding the fire bubbles echo chambers, addressing issues related to discrimination bias, and ensuring fairness in recommendations are big challenges in front of AI-based recommendation systems.

6. User feedback and evaluation 

The reviews and feedback of the users are always not up to the mark and can be influenced by various factors. So, AI tools providing recommendations based on these reviews might not be that effective solution for anyone.

7. Algorithm transparency and explainability

Most of the algorithms follow a complex process of collecting and analyzing the data and thus are considered black boxes. So, ensuring the algorithm’s transparency, explainability, and interpretability to gain the user’s trust is a big challenge in the scenario.

8. Dynamic and evolving user preferences 

User behavior and preferences are highly volatile and thus change over time. This makes it challenging to keep up with the changing nature of the user. Incorporating the regular feedback of customers, adapting the changing preferences, and maintaining accuracy is a significant challenges in the recommendation system.

9. Business and operational considerations 

Deploying the recommendation system into the business goals and then managing the computational resources is a challenging process.

10. Ethical considerations 

Ensuring ethical considerations such as fairness, accountability, transparency, and accountability is a must while deploying a recommendation system. However, the ethical use of user data and preventing biases in recommendation is a big challenge. 

Cost to Develop AI-powered Recommendation System

Just like any other application, website, or software, the cost of an AI-based recommendation system can never be constant. The cost depends on varied factors, which will hinder the cost of development as well. Let’s talk about some factors that largely determine the cost of AI-powered recommendation system development. 

1. Data collection and preparation 

As we interpreted earlier, data availability and quality is the most crucial thing that determines the performance of a recommendation system. Here, the cost of data collection can range from a few hundred to a few thousand dollars, depending on the dataset’s type, size, and complexity. 

2. Machine learning and model development 

Fostering a machine learning algorithm in the recommendation system is also a big factor consuming a major portion of the cost. The complexities of the model are directly proportional to the cost where the dollars are consumed in tasks like data preprocessing, model selection, feature engineering, and model training.

3. Infrastructure and Hosting 

The cost of servers, networking, storage, cloud services, and many other things contribute a lot to the overall cost of recommendation system development. The complexities and scales of infrastructure/hosting are the major determinants of cost. 

3. Testing and validation 

Efficient testing and validating the recommendation system with tasks like conducting experiments, A/B testing, and system performance evaluation also requires dollars from the budget. It helps ensure the accuracy and performance of the system.

4. Maintenance and Updates 

Deploying the recommendation system is not all, but ensuring maintenance and providing continuous updates is equally essential. While maintenance and update don’t come free of cost, it is worth investing in this part. 

Why Choose Appquipo for Developing an AI-Powered Recommendation System?

While you might come across several AI-development companies in the market, here are some reasons why you should go with Appquipo-

Unparalleled Expertise in AI Development	Appquipo carries unparalleled expertise in AI development when compared to its competitors. Our broad team has unmatchable expertise in artificial intelligence technology and developing the relevant tools to transfigure the business capabilities and potential.
Tailored Solutions for Your Business	Every business has its requirements, which need a specific type of solution. Before starting the project, we come into communication, understand your needs, and design a solution that fits the scenario.
Cutting-edge technologies and tools	We foster the capability of cutting-edge tools and technology in solutions to ensure competitive advantages for our clients.
Streamlined development process	We at Appquipo use agile project methodology, allowing the team member to pay keen attention to each project phase and bring out the best.
Cost-effective solutions	As we have been working in the industry for a long time, we know the areas where the cost can easily be reduced without compromising the solution’s capability.
Excellent customer support	Even after the deployment of the project, we provide ample support to our customers to manage their software and leverage the benefits.
Confidentiality and Security	Appquipo keep the confidentiality and security of our client at the center of the process. We make sure that information is kept safe between the parties.

Key Takeaways 

Recommendation systems are playing a core role in helping businesses boost their sales and increase their revenue. By suggesting the products and services to the customers according to their needs, interests, and preferences, the chances of getting the deal and bringing the cart to the billing counter increase significantly. A range of recommendation systems plays their role in recommending different types of content to billions of users worldwide. While it might seem wonderful at first glance, there are numerous complexities in developing such a solution. However, hiring a proficient team like Appquipo can help you develop a perfect solution for your business. 
Looking for a trusted AI development company to build an AI-based recommendation system? Let’s connect with Appquipo AI experts and start your AI journey today.

FAQs on AI-Based Recommendation System