Why Are Recommender Systems Expensive? A Deep Dive into Costs and Benefits

From movie streaming sites to e-commerce platforms, we all love it when we get personalised recommendations of shows or products that we like, don’t we?

Do you know what facilitates the recommendations we get on these platforms? It’s the recommender systems integrated with these platforms. For sure, a recommender system significantly improves the user experience of any platform. However, building and maintaining a recommender system isn’t cheap!

If you are wondering what makes the development of recommender systems expensive, this blog is for you. In this blog, we’ll talk about the various cost-affecting factors and how Provis Technologies, the best SaaS development company, can help you navigate these challenges effectively.

Let’s get started —

What is a Recommender System?

Before we understand what makes a recommender system so expensive, let us first learn what it is.

A recommender system, also known as a recommendation engine, as the name suggests, works as a software solution that suggests products, services or tailored content as per a user’s preferences.

Ever noticed how Netflix provides you with a personalised section based on your previous watches? That’s a recommender system for you. But a recommender system isn’t limited to movie recommendations. It can serve you from curating a personalised playlist of songs to a personalised product catalogue on an e-commerce website. 

Now, how does a recommender system work? Simply, by analysing user behaviour, historical data and preferences, a recommender system detects a pattern and provides predictions on what the user may find appealing.

Let’s understand this with an example –

Let’s say you frequently shop for gym equipment on an e-commerce site. Thus, a recommender system will help the service provider understand your preferences and recommend workout accessories that you might be interested in buying. 

Types of Recommender Systems

Recommender systems are generally of three types –

• Content-Based Systems: These systems are the ones you commonly see on movie and song streaming websites. They provide recommendations based on what the user has previously interacted with. For example, if you are someone who loves watching romantic comedies and has watched a few on a streaming site, the content-based system of that streaming site will recommend more movies in the same genre. 
• Collaborative Filtering: This system works based on the preferences of similar users. For example, if other users who have preferences similar to yours enjoy a certain product or a show, this system will also recommend that product or show to you. 
• Hybrid Models: As the name suggests, hybrid models combine both content-based and collaborative filtering systems. Consequently, a hybrid model provides more accurate and diverse recommendations.

Benefits of Investing in a Recommender System

Let’s talk about the major benefits of investing in a recommender system —

• Improved User Experience: The primary purpose of a recommender system is to provide users with an improved experience by offering relevant suggestions. 
• Increased Engagement: As users are recommended what they like, the rate of user engagement and retention considerably increases. 
• Higher Sales and Revenue: Consequent to the above points, recommender systems boost business revenue growth and drive sales by improving user experience and increasing customer engagement and retention.

Why Is a Recommender System Expensive?

It’s true that building and maintaining a recommender system isn’t cheap. After all, it’s like having a personalised assistant for every customer.  But what really makes a recommender system expensive? We can break the answer into two main domains –

Development Costs: A recommender system works on complex algorithms and a vast database. Hence, hiring a skilled team of developers is essential to obtain the best results, even if, it increases the recommender system cost significantly. 

Training and Operational Costs: In order to ensure that a recommender system works optimally, it is important to train it on vast amounts of data and use high-powered computational resources. Moreover, once everything is set up and the system goes live, it still requires maintenance and model retraining, adding on to the bill as recommender system training is very expensive.

Later in this blog, we’ll discuss the cost-affecting factors more in-depth. So, keep reading! 

What Is the Cost To Build a Recommendation System?

The cost involved in building a recommendation system is, obviously, not the same for everyone. After all, the complexity, customisation level and scale of the recommender system you want to develop affects the price. 

Let’s understand each step of recommender system development and the associated cost —

1. Planning and Requirement Analysis:  The development of a recommender system begins with planning and understanding the scope of the system. This involves assessing your business’s needs, user behaviour and data sources. 

2. Data Collection and Preprocessing: Here comes one of the most time-consuming and vital steps – gathering and cleaning data. This process can cost you immensely ranging from $5,000 to $50,000, based on the volume and quality of data you want to structure. 

3. Model Training and Optimisations: This stage is where developers stage the set for your recommender system by building and fine-tuning the algorithm for your recommendation system. Costs for this step can range from $20,000 to $100,000, as per the complexity of your system.

4. Deployment and Maintenance: Lastly, it’s time to deploy the recommender system and integrate it with the platform. This process can range from $10,000 to $50,000. Wait, it doesn’t end here. There will also be ongoing maintenance costs, typically around 10-20% of the initial development cost per year. 

Cost Breakdown for Different Systems

Here’s an overview of the development cost of a recommender system based on scale —

What Are the Factors That Affect the Cost To Build a Recommendation System?

There are several factors that affect the cost of development of a recommender system — 

1. Data Requirements: So far in this read, we have made it quite clear that the volume, diversity and quality of data you want the recommender system to train on, play a crucial role. Simply, the larger the volume and the more diverse the data is, the more expensive the cost of development will be. 

2. Algorithm Complexity: If you are going to build a simple recommender system ( let’s say collaborative filtering model), the costs of development will be less expensive as compared to hybrid models. After all, a hybrid model will need a complex and advanced algorithm. 

3. Computational Resources: How much computational power your recommender system requires also affects the development cost. For instance, cloud-based solutions are more cost-effective and scalable as compared to on-premise infrastructure, which requires significant upfront investments in servers and hardware. 

4. Integration and Customisation: If you want your recommender system to properly integrate with your platform, it will need customisation, extra time and resources, adding up to the development cost. 

Conclusion

Building a recommender system is surely an expensive journey. However, you can’t ignore the fact that having such a system on your platform can significantly help you boost your business’s overall growth. Also, knowing the cost-affecting factors in mind, you can plan smartly and reduce the cost of development.

Frequently Asked Questions

Q: How to build a custom recommendation system? 

In order to build a custom recommendation system, you’ll have to: gather relevant data > select the right algorithm > train the model > customise and integrate it with your platform. 

Q: How much does it cost to build a recommendation engine? 

The overall cost to build a recommendation engine depends on various factors such as complexity, data volume and quality, expertise needed, etc. It can range from $10,000 to $300,000+.