How to Build an AI Product Recommendation System for Retail

In retail, leading the market means modernizing all your operations. The industry’s leaders, including Amazon and Netflix, use artificial intelligence technology to offer an individualized shopping and viewing experience, thereby increasing their sales. However, not every company has the expertise to replicate this success.

Over the past 6 years leading AI development, I’ve seen how both startups and established companies face difficulty in building a recommendation system. Startups often struggle with limited resources and expertise, which can slow innovation and delay product launches, while larger retail companies deal with talent shortages and the difficulties of integrating advanced AI into their existing systems while managing tight budgets.

In this article, I’ll walk you through the process of building AI recommendation systems, addressing these challenges with practical solutions. With MobiDev’s extensive experience in building recommendation systems using machine learning and AI, and my own leadership of successful AI projects, this guide will help you create scalable, effective solutions that align with your business objectives.

What is an AI-powered Product Recommendation Engine?

An AI-powered recommendation engine is a tool that suggests goods or services or even content based on the tastes and preferences of a user. Such systems are used by Amazon, Netflix, and Spotify to increase user interference and improve business performance.

At its core, an AI recommendation system works with a variety of user information including, browsing history, previous purchases, etc. This information is key to understanding what users want. Take, for example, Amazon, known for using a certain algorithm to recommend products based on what you purchased previously. Spotify, in its turn, recommends music based on patterns in your listening habits.

Once relevant data is gathered, it’s stored in cloud systems to keep things running smoothly and scale up as needed. Platforms like AWS or Google Cloud can handle this large volume of data. Then, the data is analyzed with machine learning tools, statistical methods, and natural language processing (NLP) techniques. This step is necessary to find patterns that help predict what a user will like next and define the proper form of technical realization of the recommendation engine that will cover business goals.

From there, the data is filtered to generate recommendations. This is the foundational concept of how to build a product recommendation system that evolves as more user data is collected. AI engines typically use techniques like collaborative filtering, content-based filtering, or hybrid models (we will discuss them and some other techniques in detail later).

Overall, these systems not only enhance user satisfaction but also drive business growth by delivering highly personalized experiences. In fact, using a recommender system with machine learning and artificial intelligence allows companies to build more accurate models over time, refining their recommendations as they learn from user interactions.

TOP 5 Benefits of AI Recommendation Systems for Retail

With the use of AI-powered recommendation systems, a customer can receive exactly what they need. This is because the AI can interpret and understand who the customer is. This empowers businesses in the retail space to move ahead and stand out among others. Let’s explore the game-changing benefits AI can bring to your retail business.

1. Enhancing Customer Experience

It’s no secret that customers crave personalized shopping experiences. Based on my experience with both startups and large retail brands, I’ve found that AI can play a key role in achieving this by delivering tailored recommendations and automating the entire process. When a person uses a search engine and enters a few defining phrases about a product, an AI will recall the user’s past purchases, model the most fitting suggestions, and make them available to the user.

Instead of looking through endless options, customers can see items that match their style, size, and color. This way, their shopping experience will be faster and more relevant. In fact, AI recommendation systems can also be integrated into mobile applications, social media, and emails, ensuring a personalized experience across all touchpoints and keeping customers engaged.

2. Boosting Sales and Revenue

Knowing how to build a recommendation engine can directly improve conversion rates. For example, when a customer is searching for a smartphone, the system can recommend related products such as cases or headphones. These customized AI product recommendations facilitate upselling and cross-selling opportunities, which enhances the customer’s shopping experience and subsequently results in increased profits.

AI assists retailers in optimizing inventory and pricing based on consumer behavior and market trends. For instance, if demand for eco-friendly products rises, AI can help adjust stock levels, which ultimately increases sales and prepares businesses for market changes.

3. Building Customer Loyalty

Gaining customer loyalty is tough, but AI recommendation systems can help. Personalized suggestions build trust, strengthening customers’ connection to a brand.
Companies such as Netflix and Amazon use AI to make customers come back for more. For retailers, embracing a personalized shopping experience means greater customer loyalty and, consequently, an increase in purchases.
With the right system, personalization becomes an automatic part of the customer journey, making loyalty feel like a natural outcome.

4. Streamlining Operations

Retailers leveraging artificial intelligence in recommender systems can reap significant operational benefits, offering insights that help them make smarter decisions around inventory, marketing, and forecasting.
AI not only enhances customer insights but also streamlines internal processes, boosting overall efficiency.
In today’s world, it is clear that artificial intelligence is crucial in optimizing processes. Businesses that do not employ AI have more chances to fail as they will not be able to keep up with the competition.

5. Staying Competitive in a Data-Driven Market

AI systems customize data for both retailers and consumers, transforming it into actionable insights. This allows retailers to interact more effectively with consumers and suit their needs.

Moreover, AI significantly reduces time spent on business operations like marketing campaigns or forecasting trends. This efficiency provides a competitive advantage over businesses still deciding to stick to traditional methods.

AI-powered recommendation systems come in different types, each tailored to address specific needs and leverage various approaches for product suggestions. Working with AI in the retail industry, I’ve come to understand how diverse recommendation techniques can shape both customer engagement and business success.

Here’s an overview of the main types of AI recommendation systems: collaborative filtering, content-based filtering, hybrid systems, deep learning, and knowledge-based systems.

1. Collaborative Filtering

Collaborative filtering suggests items based on the preferences of users with similar tastes. It includes:
User-based filtering: Identifies users with shared interests. For instance, a user who likes certain movies may get recommendations based on another user with similar preferences.
Item-based filtering: Focuses on product similarities, like recommending headphones to someone who purchased a smartphone.

While effective, this process can struggle with sparse data and scalability as user bases grow.

2. Content-Based Filtering

Content-based filtering analyzes the attributes of items a user has interacted with in order to recommend options with similar features. For instance, if a user has purchased a mystery book before, then the system will recommend some other mystery novels to that user.

Understanding how to build an AI powered recommendation system in this manner allows businesses to offer personalized suggestions based on product features, even for niche categories or in cases with limited interaction data.

3. Hybrid Systems

Hybrid systems combine collaborative and content-based methods for improved accuracy. For instance, Netflix combines viewing history with content attributes to recommend shows, while Amazon blends user behavior with product features to suggest purchases. This approach ensures highly personalized suggestions.

4. Deep Learning

Deep learning leverages neural networks to uncover complex patterns in large datasets, enabling highly personalized recommendations. It excels at detecting subtle preferences, like linking love for horror and comedy to dark humor, analyzing unstructured data such as images or text without manual intervention, and delivering accurate suggestions even for new users with minimal input.

Deep learning techniques enhance recommendation systems:

• Neural Collaborative Filtering (NCF): Predicts user-item interactions through embeddings and neural layers
• Recurrent Neural Networks (RNNs): Ideal for session-based or sequential recommendations
• Convolutional Neural Networks (CNNs): Process content-rich data like images or text
• Transformers: Delivers context-aware suggestions through advanced sequence modeling.

These capabilities make deep learning indispensable for large-scale platforms, enabling nuanced and contextually relevant recommendations.

5. Knowledge-Based Systems

These systems recommend items using explicit user inputs and domain-specific rules. For instance, a real estate platform might suggest properties based on budget, location, and preferences. These systems avoid cold-start issues but require extensive data and development investment.

Knowledge-based recommendation systems are often considered the most complex approach due to their reliance on extensive data from various sources. If a retailer doesn’t have a large enough dataset, this method may not be suitable.

7 Steps to build an AI-Driven Product Recommendation System

Building AI-driven recommendation systems is a multi-step process that requires careful planning, data handling, and continuous optimization. Based on my experience with AI solutions for retail and insights from the field, I’ll walk you through each step here.

Step 1. Business Analysis

Prior to delving into the technical details, it is important to clarify what the objectives of your business are and what results you wish to achieve. This step involves determining what the recommendation system is for: more sales, better customer retention, or enhanced shopping experience. Defining the target audience and KPIs is very important. These may consist of such parameters as click-through rates, average order value, or customer satisfaction scores. By aligning the system’s objectives with business goals, you create a solid foundation for success.

Step 2. Data Collection and Processing

Data is the lifeblood of any AI recommendation system project. To start, you should first collect implicit data (e.g., sales data, browsing history, purchase patterns, and search logs). While collecting explicit data is also valuable, it’s not strictly necessary. Behavioral data, such as clicks and cart activity, is relatively straightforward to gather, but it often requires careful filtering to remove noise and ensure relevance.

After analysis, it’s crucial to choose the right model (e.g., content-based, collaborative filtering) based on the data type and business goals. Equally important is ensuring the data is free from anomalies, as this can skew results and impact the effectiveness of the recommendation system over time.

Step 3. Choosing an Approach to Development

Understanding how to build a recommender system​ also involves selecting the right approach. Based on the available data, you can start with:

• Content-based filtering
• Collaborative filtering
• Hybrid models

For businesses just starting out, I often recommend a content-based approach since it’s easier to implement and requires less initial data. As your database grows, you can evolve into more complex models like collaborative or hybrid systems to improve performance and scalability.

Step 4. Data Modeling and Training

This step involves training your recommendation model to recognize patterns and relationships within the processed data. Depending on your chosen approach, the model learns from historical user interactions, product features, or both.

During this phase, the dataset is typically split into training, test and validation sets to guide the model’s learning process and ensure it generalizes well for unseen data. Based on desired business metrics, the initial evaluation metrics could be selected as follows: F1 score, precision or recall for this stage to monitor progress and identify areas where adjustments to the model or parameters are needed. However, the primary focus here is to establish a solid baseline for the recommendation system’s performance.

Step 5. Model Evaluation and Fine-Tuning

Once the initial training is over, the model performance undergoes an in-depth assessment. In this step, A/B testing is very useful because you can test many recommendation strategies and see what resonates best with users. Changes can be anything from tweaking parameters to revisiting earlier steps in data processing or modeling. This iterative approach ensures the system delivers both accuracy and business value.

Step 6. Integration and Deployment

Once the data is collected, it’s processed using one of three analysis methods:

1. Real-time analysis for instant recommendations during a session
2. Batch processing for periodic updates, like daily recommendation emails
3. Near-real-time analysis for frequent updates within a browsing session

Moreover, knowing how to build a recommendation system involves more than just designing the model—it’s about seamlessly integrating it into your application or platform. Depending on your system architecture, you can choose:

• API-based integration for flexible interaction with other systems
• Embedding as a microservice for scalability and modularity
• Direct integration for smaller-scale applications

Deployment has to focus on scalability and real-world performance to ensure that the system can cope with the load. At this point, regular observations are very critical to solving any problems and providing an impeccable user experience.

Step 7. Monitoring and Improvement

Building a recommendation system is not a one-and-done effort. User preferences evolve, and so should your system. Continuous monitoring ensures the model stays relevant and effective. Regular updates with new data, combined with periodic retraining, help adapt to changes in user behavior and product offerings.

A/B testing remains a valuable tool for testing new algorithms or strategies, allowing for iterative improvement over time. By staying proactive, you can keep your recommendation system aligned with both user expectations and business goals.

TOP 5 Challenges and Best Practices for Building a Recommendation System

Creating a recommendation system is a complex process, often requiring a balance between technical feasibility and user experience. Throughout the process, several key challenges may arise. However, with a strategic approach and adherence to best practices, these obstacles can be transformed into opportunities for success. Let me guide you through these critical challenges and provide effective solutions MobiDev AI team uses to overcome them.

1. Data Consistency

Challenge: The relevance of recommendations depends heavily on the consistency and context of the training data. For example, if a retailer operates multiple stores but trains the model using data from only one location, the recommendations may only be accurate for that specific store.

Another example might be a retailer with stores in both a busy city center and a suburban bedroom community. The city center may have high foot traffic but smaller purchases, while the suburban store may see fewer customers but larger transactions. Using the same model on data from both stores would lead to inaccurate recommendations, as it overlooks the distinct purchasing behaviors of each location.

Best Practice: Our expert team ensures that data reflects the full scope of operations. For multi-store retailers, we aggregate and normalize data across locations while accounting for regional variations. This guarantees that recommendations are relevant across the entire network.

2. Cold Start Problem

Challenge: New users or products often lack sufficient historical data for collaborative filtering to be effective. This can lead to inaccurate or irrelevant recommendations.

Best Practice: We address this by combining collaborative filtering with content-based and if possible, knowledge-based methods. For new products, we use attributes such as descriptions, categories, and specifications to generate recommendations. For new users, we collect preferences through onboarding questionnaires or leverage demographic data to offer initial suggestions. As user interactions increase, the system adapts to individual behavior.

3. Scalability

Challenge: Many algorithms perform well on small datasets but struggle to maintain accuracy and efficiency when scaled to millions of users and items. Computational costs can also rise significantly with larger datasets.

Best Practice: Our experts design the system to scale efficiently, using distributed computing frameworks and optimizing algorithms for large datasets. We employ techniques like approximate nearest neighbor (ANN) search or matrix factorization to enhance computational efficiency, ensuring the system performs consistently at scale.

4. Data Privacy and Security

Challenge: Protecting user data while delivering personalized recommendations is a critical concern, especially with increasing privacy regulations and user awareness.

Best Practice: We adhere to the strictest data privacy and security protocols to protect user information. Our team ensures compliance with privacy regulations such as GDPR and CCPA, encrypting sensitive data both in transit and at rest. We also implement role-based access controls, ensuring that only authorized personnel can access user data. Regular security audits and user consent management processes are also in place to ensure that data is handled responsibly, while still delivering personalized recommendations.

5. Model Improvements

Challenge: User preferences and product availability change over time, requiring the recommendation system to stay up-to-date. A static model may become less effective as trends and behaviors evolve.

Best Practice: We embrace continuous learning and incorporate online learning methods to update models with new data or retrain them to keep up with changing trends. To make the recommendations more relevant, we embed contextual data like time of day, location, or type of device. Continuous monitoring and A/B testing will assist in fine-tuning the system and meeting user expectations.

Success Story: Building a Recommendation Engine for a Complex Retail ERP & POS System

ComCash is a US-based ERP software company specializing in retail solutions. Our collaboration with ComCash began in 2013, and over the years, we have integrated AI modules for demand forecasting, statistical reporting, and product recommendations. This long-term collaboration culminated in ComCash’s acquisition by POS Nation in 2022, after successfully developing a robust ecosystem of tools.

Client Goal: MobiDev was tasked with overhauling the legacy ComCash system, transforming it into a sophisticated, cloud-based retail ERP platform with advanced capabilities to support evolving business needs.

How We Delivered:

1. System Revamp and Development Cycle Management: Our team began with comprehensive consulting, crafting detailed project documentation that clearly defined requirements, mitigated risks, and prioritized key features for the first version of the application. Once the optimal technical strategy was finalized, MobiDev managed the entire development cycle, including app design, ensuring the system was built to meet modern retail demands.

2. Demand Forecasting Integration: We implemented an adaptive AI demand forecasting model that analyzes users’ retail sales data to predict demand for the upcoming weeks. By processing historical data and integrating external inputs, the model identified high-demand items. Its adaptability enables forecasting for specific products at any given time, ensuring accurate predictions for retailers.

3. Smart Product Recommendation Engine: MobiDev developed a recommendation engine using associative rules and the Apriori algorithm to suggest complementary products based on customer purchasing patterns. This engine was part of a broader AI-driven backend system that integrates seamlessly with the ERP, providing actionable insights and enabling users to make informed business decisions.

[Together with MobiDev], we’re able to work on a 24-hour development cycle, and we release software repeatedly faster than any of our competitors — and there is no overtime. We could never create what we have with MobiDev in my office in California. The tech market is just too competitive these days. If you are interested in developing a world-class product and working with a great group of friendly co-workers every day, I wholeheartedly recommend MobiDev.

Richard Stack

CEO of Comcash Inc.

Why Choose MobiDev For AI Recommendation System Development

Operating in the software development market since 2009, MobiDev has been at the forefront of AI development since 2018, helping businesses across industries harness the power of AI. We specialize in both integrating pre-trained AI solutions and developing custom AI systems tailored to meet the unique needs of each client. With dedicated in-house AI labs focused on research and innovation, MobiDev ensures that its recommendation systems are designed to deliver maximum impact for retail businesses.

If you’re looking for guidance on how to build a product recommendation engine, MobiDev offers a comprehensive range of services, from expert AI consulting to full-scale development, supporting clients through every stage of the process. Whether integrating AI into existing systems or building a solution from scratch, MobiDev’s team works closely with clients to deliver highly effective, data-driven recommendation engines. To explore how MobiDev can assist with retail software development services, reach out to our expert team.