Why Build a Custom Content Recommendation Engine? - Lemberg Solutions
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Why Build a Custom Content Recommendation Engine?

Content recommendation engines help you captivate readers’ interest, improve clickthrough and conversion rates, and increase revenue from page ads by up to 20%. But deciding whether to purchase an existing content recommendation system – such as Taboola or Outbrain – or build your own can be a real challenge. 

Commercial systems work best for businesses with simple and straightforward goals. If you know that your needs are not going to exceed what paid systems have to offer, there’s no harm in purchasing one. 

However, as your business grows, you might find your commercial solution lacks multilingual support and fine-tuned analytics or doesn’t offer as much control over recommendation accuracy as you might have expected. And although you’ll need a bit of preparation to develop a custom content recommendation engine, it might be well worth it if commercial products don’t satisfy your needs. 

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You should build a custom content recommendation system if:

  • Commercial systems fail to deliver exactly what you’re looking for. 
  • You want to have control over what your marketing tools can and cannot do. 

Let’s look at a few other cases when custom might be better than ready-made.


Existing solutions don’t have the features you need

Companies behind content recommendation SaaS products strive to reach a wide audience, and creating niche features won’t get them there. If your business is more of an edge case – with goals and approaches different from other businesses in your segment – you won’t benefit from the average feature sets. 

In this case, you might be better off developing a bespoke system with tools that can help your business thrive. 

You publish for a multilingual audience

Although research into natural language processing (NLP) has come a long way, most commercial solutions still only work with content in English. However, if you publish in a different language or have a multilingual platform, chances are you still want to offer accurate content recommendations. 

This was the case with Monda Magazin, one of our clients. Their online publication is entirely in German, and part of the issue they had with paid content recommendation systems was the inability to accurately analyze German text. To solve this, our team extended and customized existing NLP framework and libraries to build algorithms that can analyze German-language content. 

If you face similar difficulties, building a custom solution is your best bet. This way, you can reap the benefits of smart content recommendations while connecting with your audience in a language they understand.

You want a more accurate content recommendation algorithm

Commercial content recommendation algorithms might not be accurate enough for your business. Most commercial content recommendation systems implement the statistical twins model, i.e. they group users by shared attributes. Such algorithms find out that a thirty-something-year-old woman from the UK enjoys reading articles about retro car engines, for instance, and then suggest content about retro cars to other thirty-something-year-old women in the UK. 

The statistical twins approach can work well if you publish content about one or two narrow topics to an audience you know well. However, you’ll see a significant drop in accuracy when you begin extending your audience or branch out into new content areas. 

Monda Magazin also had trouble with the accuracy of existing content recommendation algorithms. To solve this challenge, our data science team designed a complex hybrid algorithm that processes article keywords and cross-analyzes them with 36 data points about each Monda reader. With the current state of commercial content recommendation technology, your only way to achieve high recommendation accuracy is to build a custom algorithm. 

You don’t want third-party products to collect data about your audience

Purchasing commercial content recommendation engines comes with a caveat: Third-party products will either collect data about your readers or request access to data you’ve accumulated over time. 

There’s no way around third-party products processing your reader data, as they need data to work. If you don’t want to share data about your audience with third parties, building a custom solution might be your only option. 

Customizing an existing solution doesn’t work for you

While some ready-made content recommendation systems can be customized, the options are often limited. Some systems are architected in a way that doesn’t leave much space for you to customize their features. Or you might find that the size of your business makes customization more expensive than building a solution from scratch. 

In case customizing a paid content recommendation engine is something you can’t or don’t want to do, building your own system is a valid option. 

When considering developing a custom content recommendation system, keep in mind that software development takes time and preparation. Let’s look at a few things you will need to do prior to developing a content recommendation AI. 

To develop a bespoke content recommendation system, you should: 

Find the time to focus on development, understand your audience and what you’re trying to achieve, and collect and prepare relevant data on readers. 


Ensure that your business is in the right place

You’ll need time to focus on developing a custom content recommendation AI system. Such a system may take months to build and train properly, and you should take that into consideration when deciding to build one. 

It might be smart to start with a paid content recommendation engine and then move on to building a custom one. You can use a commercial solution while building a custom one even if the recommendation quality of the commercial system isn't as good as you'd like. 

Take time to understand your goals and your audience

The main attraction of commercial solutions is that you don’t need to think about designing, developing and maintaining software: just buy or subscribe to a product and reap the benefits. Custom development is different — you’re responsible for building a toolkit that you can benefit from. 

To build your own recommendation system, start by studying existing solutions and looking at the typical behavior of your audience. This should help you decide what kinds of features you might need: a tool to monitor audience activity like with Google Analytics, a tool to customize how many articles to suggest, a tool to place different ads on different pages, and so on. 

Once you have a more or less complete list of features, talk to software development companies that have already built content recommendation systems. Most web development services companies have business analysts to help you define clear product requirements and crystalize your expectations into a concrete product development plan. At Lemberg, Anastasiia Lepuha creates user stories and product requirements. You can learn more about Anastasiia in one of our LinkedIn posts. 

Start collecting and preparing relevant data about your readers

Smart solutions require quite a bit of quality training data. If you have a data engineer on your team, start collecting and preparing data on your readers as soon as you can: the more data you give to your data scientist, the better the algorithm they'll be able to design for you. 

You can learn more about collecting and preparing training data for AI in our article How to Prepare Training Data for Better AI?


  • Content recommendation engines can offer a 20%+ boost to revenue from page ads as well as to your clickthrough and conversion rates. 
  • Commercial content recommendation engines work best for businesses with simple, straightforward goals. 
  • If you find commercial solutions lacking, you can build a custom solution that perfectly meets your needs. 
  • To build a quality recommendation system, prepare thoroughly: allocate enough resources, analyze existing solutions, understand your audience, and collect relevant data. 
  • Developing a custom content recommendation AI isn’t easy, but it might be worth the effort if existing solutions don’t help you achieve your goals. 
  • Learn about the custom content recommendation engine we built at Lemberg
  • To decide whether to build your own custom recommendation system or purchase a ready-made system, consider your goals. If existing products aren’t good enough to help you reach those goals, develop your own software that does exactly what you need. 
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