How to Build a Recommendation System: Practical Tips and Use Cases

In this article, we’ll explain what algorithms recommendation systems are based on, examine before-and-after cases from different industries, and suggest an implementation strategy.

Components and Algorithms

To build a recommendation engine means to establish an AI-backed algorithm that will analyze the user’s previous orders, indicate how interested the user is in certain products, and automatically suggest similar products. Here’s how machine learning for recommendations works:

1. User interacts with the website by browsing, searching, clicking, and purchasing items.
2. The system gathers data on user behavior, such as what they buy, search for, click on, and spend time looking at.
3. It compares the user’s behavior with similar users to find patterns.
4. Based on these patterns, the system suggests items the user might like.
5. As the user continues to interact with the website, the system learns and improves its suggestions to better match the user’s preferences.

Why recommendation system is important for businesses? The fact is that AI-supported applications use different methods to arrive at the most accurate and personalized recommendations possible. Let’s examine a few of the most widely used algorithms.

1. Content-based recommendations

Content-based AI algorithms find recommendations by evaluating the products the users purchase. The information, such as tags and metadata of those products, is analyzed. This method can achieve good results, especially if not much information about the customer is available. A statement that could be used to describe these systems would be: «Show me more of what I liked before.»

An example is an online shop, where customers could be offered special kitchen instruments if they have just moved a specific cookbook to their shopping cart.

2. Recommendations based on popularity

This type of recommendations implies offering the most popular products to the user.  It is particularly suitable when there is little information about both the user and the products. A statement that could describe these systems would be: «Show me more of what everyone liked.»

For instance, when a new user signs up for Netflix and has not yet watched any content, the platform may initially recommend popular movies or TV shows based on overall viewing trends or ratings from other users. Similarly, suppose a user hasn’t provided much information about their music preferences on Spotify. In that case, the platform might suggest popular songs, albums, or playlists trending globally or within a particular genre.

3. Collaborative algorithms

This term describes those AI algorithms for recommendations that achieve results by comparing different user profiles. The customer’s user history is analyzed and compared with all other users. 

A statement for this technique might be, «Tell me what my neighbors like because I might like it too.» You can notice collaborative filtering recommendation algorithms in E-commerce platforms like Amazon. This platform analyzes users’ purchase history, ratings, and browsing behavior to identify patterns and similarities among different user profiles. By leveraging collaborative filtering, these algorithms can recommend products, movies, or TV shows to users based on the preferences and behaviors of similar users, thereby enhancing personalized recommendations and improving user satisfaction and engagement.

Collaborative filtering can be divided into two categories: memory-based and model-based.

Memory-based recommendation system has two characteristics:

• It uses statistical methods to approximate users or elements, such as cosine distance (a measure of similarity between two vectors in a multidimensional space) or Pearson correlation (a measure of the linear correlation between two variables).
• It uses the entire user-item record to create a recommendation.

Being model-based, the system:

• Develops a model of users to learn their preferences
• Can be built using machine learning techniques such as regression, clustering, classification, etc.

A major advantage of collaborative filtering is that it works well even when there is not much content associated with the articles or when it is difficult for a computer system to analyze them (e.g., opinions). Additionally, this technique can recommend relevant articles to the user, even if the content is not included in the user’s profile.

4. Hybrid algorithms

Basically, depending on the business model and available data, you have to decide which algorithms are best suited to optimize recommendations for your customers. In the vast majority of cases, however, we use mixed forms. 

For example, collaborative algorithms are often displayed based on the popularity of the respective products. It requires a thorough analysis of the individual situation as well as an extensive testing phase to determine which methods can be used most effectively for your business.

Consider leveraging AI advancements in recommendations to achieve personalization at scale for your website. By harnessing predictive analytics services, you can deliver tailored recommendations based on past user behavior, enhancing user experience and engagement.

The Impact of Recommendation Systems

The introduction of recommendation systems on websites has allowed users on both sides of the spectrum to engage with content in an entirely new way. We’ve picked a few examples from different industries so you can easily compare the before and after. 

Use Case	Before Implementing Recommendation System	After Implementing Recommendation System
E-commerce: Personalized outfit styling	Users browse individual clothing items without guidance, resulting in time-consuming searches and limited cross-selling opportunities.	Upon logging in, users are prompted to create a style profile by selecting their preferred colors, styles, and occasions. The recommendation system then generates complete outfit suggestions based on their profile and past purchases, enhancing user experience and increasing average order value.
Video streaming: content discovery	Users rely solely on browsing categories and trending lists to discover new content, often overlooking personalized recommendations.	Upon login, users are presented with a personalized homepage featuring recommended movies and TV shows based on their viewing history, preferences, and ratings. The recommendation system continuously updates recommendations based on user interactions, leading to increased content consumption and user satisfaction.
Publishing: Personalized article suggestions	Users manually navigate through various sections and articles, often missing out on relevant news topics or diverse perspectives.	Upon login, users are presented with personalized article suggestions based on their reading history, interests, and trending topics. The recommendation system dynamically updates suggestions based on user engagement and feedback, facilitating diverse news consumption and enhancing user satisfaction.
Fitness app: Tailored workout plans	Users navigate through generic workout routines, struggling to find exercises that match their fitness goals and preferences.	After completing a fitness assessment, users receive personalized workout plans tailored to their fitness level, goals, and preferences. The recommendation system adapts workouts based on user feedback, progress, and performance, providing a customized fitness experience and motivating users to achieve their goals.

These examples show how helpful the use of recommendations is. And these become true killer applications when they are tailored to customers using artificial intelligence. This way, you can significantly improve the accuracy of these recommendations. How to evaluate recommendation system’s performance? Key success evaluation metrics of a recommendation system as a software tool include:

• User engagement: Measure how well the system predicts user preferences and suggests relevant items. Assess the level of user interaction with recommended content, indicating the system’s ability to capture interest and encourage exploration.
• Conversion rate: This rate will reflect the percentage of users who take desired actions after interacting with recommendations, driving revenue generation. Evaluate how well recommendations align with user preferences, promoting diverse and engaging content discovery.
• Iterative feedback assessment on a business level: Utilize user feedback to iteratively enhance recommendation algorithms and user experience, ensuring ongoing optimization and relevance.

Major Challenges: Data Collection and Preprocessing

However, businesses may face two issues: missing data and the need to standardize the incoming data flow. Here’s an instruction you may follow if any of these issues occur: 

Dealing with missing data

1. Identify and understand missing data:

• Conduct a thorough analysis to identify where missing data exists in your datasets.
• Understand the reasons behind missing data, whether due to data entry errors, system failures, or user behavior.

2. Implement data imputation techniques:

• Use statistical methods such as mean, median, or mode imputation to fill in missing values for numerical data.
• For categorical data, consider imputation techniques like using the most frequent category or employing ML algorithms for prediction.
• Explore advanced imputation methods such as interpolation for more accurate filling of missing values.

3. Monitor data quality and update procedures:

• Regularly monitor the quality of your data and assess the effectiveness of your imputation techniques.
• Update and refine your data collection processes to minimize the occurrence of missing data in the future.
• Implement data validation checks and establish protocols for handling missing data in real time to maintain data integrity.

Conducting data normalization

1. Understand the data distribution:

• Determine the appropriate normalization technique based on the characteristics of your data, such as whether it follows a Gaussian distribution or has heavy tails.

2. Apply normalization techniques:

• Analyze the distribution of your data to identify any skewness or outliers that may affect normalization.
• Utilize Min-Max scaling to rescale the values of numerical features to a specific range, typically between 0 and 1, preserving the relative relationships between the data points.

3. Validate and assess normalized data:

• Evaluate the impact of normalization on your data’s performance in predictive modeling or analysis tasks.
• Monitor the range and distribution of normalized features to ensure they align with the requirements of ML-based algorithms.
• Conduct sensitivity analyses to assess the robustness of your normalization techniques to variations in data characteristics and model performance.

Whether you start from user desires or the similarity of products or elements, these initial steps can help your recommendation systems better meet your business goals.

Other Challenges and Considerations

Depending on the application and objective, recommendation algorithms may be associated with a few challenges. Lightpoint specialists supplemented these problems with possible mitigation tips to help you take timely measures.

1. Cold start problem

Lack of historical data or user information for new users or items leads to a so-called cold start problem. It arises when the recommendation system struggles to provide accurate and personalized recommendations to new users or items due to insufficient data.

Mitigation tip: Implement hybrid recommendation approaches that combine content-based and collaborative filtering techniques to mitigate the cold start problem. Data analytics solutions can help leverage user and item attributes for initial recommendations.

2. Scalability

The increasing user base and growing volumes of data can overload the website. This challenge occurs when the recommendation system struggles to handle large amounts of data and increasing numbers of users while maintaining response times and performance levels.

Mitigation tip: Utilize distributed computing frameworks and scalable infrastructure solutions to process and analyze vast datasets efficiently. This ensures the recommendation system can scale horizontally to accommodate growing user bases and data volumes.

3. Performance

A personalized recommendation system’s increasing complexity can negatively impact user experience when the system fails to deliver recommendations within acceptable response times.

Mitigation tip: Employ optimization techniques such as algorithmic improvements, caching, and parallel processing to enhance the efficiency and responsiveness of the recommendation system, ensuring the timely delivery of personalized recommendations to users.

4. Ethical implications

Ethical implications in recommendations encompass issues related to algorithmic fairness, privacy violations, and the propagation of harmful or inappropriate content through recommendations, which can erode user trust and lead to social and legal repercussions.

Mitigation tip: Implement transparent and accountable recommendation systems that prioritize user privacy, mitigate biases through diverse training data and algorithmic fairness measures, and provide mechanisms for users to control and understand how their data is used for recommendations. Regularly audit and evaluate the ethical implications of recommendation algorithms to ensure alignment with ethical guidelines and regulatory standards.

Implementing a Personalized Recommendation System

1. Collect and prepare the data to be recommended. Gather relevant data sources such as user interactions and demographics. Clean and preprocess the data to ensure quality and structure.

2. Select and develop a proper algorithm. Choose suitable recommendation algorithms based on data and objectives.Develop and train recommendation models, optimizing data preprocessing and user-item interactions.

3. Deploy the system and set up continuous monitoring. Adopt and embed the recommendation system into existing infrastructure simultaneously with business strategy integration.Ensure seamless interaction and scalability for real-time recommendations.Establish key performance indicators (KPIs) to measure system effectiveness and adopt continuous improvement strategies.

Conclusion

AI-driven recommendations increase user engagement and conversions by presenting users with content or products that resonate with their interests and needs. By delivering timely and relevant suggestions, recommendation systems capture user attention, encourage exploration, and facilitate desired actions such as purchasing or consuming additional content.

Learning how to build a recommendation system entails mastering data preprocessing techniques, algorithm development, and evaluating system performance to ensure optimal offerings. Our experts will provide tailored guidance and development if you consider using AI-based recommendations for your company.