YesPlz Product Recommendation System vs. Others

The Difference Between Product Recommendation Systems

Jess Erdman, November 2021

The best things in life take time. And, when it comes to product recommendation systems, taking an extra few minutes can make all the difference in showing your customers relevant, accurate product recommendations. 


Each product recommendation system is different, based on the underlying technology that supports each. Unsurprisingly, product recommendation systems are becoming more and more accurate based on new technologies. 


In this article, we’ll go over:

  • A history of product recommendation systems
  • Visual search for product recommendations
  • What happens behind the scenes when you install the YesPlz Product Recommendation System on Shopify

The YesPlz Product Recommendation System is built to understand your store’s products and make fashion-forward recommendations. You can learn more about how our Product Recommendation System works in this blog.

Let’s dive into product recommendation systems.

A history of product recommendation systems


In the past, product recommendation systems were based on matching the texts of a product title to a recommended product. So, if your customer were to search for the word “boots,” they would see recommended products that are boots. Easy, right?

But, once we begin to introduce more complex search terms into the mix, it becomes proportionally more complicated. For example, if your customer were to search for the term “white boots,” they might receive a mix of recommended products--some which are white, and some which are boots. 


Text-based search is limited to the quality of the keywords. The customer searching for a product needs to know which search terms to type in. And, on the other side, the eCommerce company needs to have a system that can recognize both keywords and search intent. 


The complicated fashion taxonomy behind text-based search can limit the success of a product recommendation system. If your customer is searching for a white boot and receives recommended products that include a running sneaker, it’s unlikely that they will click through to an entirely different product.


Product recommendations should be within the same category and/or complementary to the search--for example, a winter jacket search might return winter accessories or other winter jackets in a similar style. 


The sheer complexity of different product attributes (for example, a white boot description could include heel height, type, and material). Any combination of these text-based keywords could create a different set of product recommendations, depending on which keywords are selected. 


Keyword-based product recommendations are unpredictable, especially as product searches become more complex.


And, from the point of view of the retailer, it’s overwhelming to create product descriptions. It’s hard to create a balance between product descriptions that are easy for a keyword-based search engine to understand--and to create product descriptions that are engaging and fun for customers to read. You shouldn’t have to make a trade-off that can affect customer engagement.


What’s the solution for a problem with serious consequences, such as customers not completing sales or bouncing from websites?. 


The advantages of visual search for product recommendation systems


Keyword-based search is burdensome for customers and retailers. Enter visual search.


No more vague contextual clues for a computer to likely misinterpret. No more strange product recommendations that include inaccurate sub-categories of the original search. 


Visual search can completely change the course of product recommendation systems. Customers are more engaged and more loyal. Through visual search, customers can also experience online shopping that’s closer to in-store. All in all, visual search has countless advantages, especially when building a product recommendation system.


When setting up a product recommendation system, whether text-based or visual, the technology needs time to scan products to create the recommendations. Text-based search can take a few minutes to learn 100 products. Visual recognition can take up to 10 minutes to learn 100 products.


But, back to the original question:

Would you rather have product recommendations that are accurate (and takes 10 minutes to set up) or inaccurate (with a few minutes of set-up time)?

The answer is obvious--the best things in life (and product recommendations) take a few minutes more to set-up. 


What happens behind the scenes when you install the YesPlz Product Recommendation System on Shopify


When you first install the YesPlz Product Recommendation System on Shopify, you’ll be greeted with a screen to get started and connect your store. You also have the option to learn more about the YesPlz Product Recommendation System.





After you connect your store, our artificial intelligence will study your store’s products in order to make the best possible recommendations to your customers. By taking the time to truly learn your store’s products, our product recommendation system will be able to give quality recommendations that lead to higher click-through rates. 





Your product data will be synced with YesPlz AI, and finally, you’ll decide where to display the recommendations and be able to customize the types of recommendations:





The final recommendations displayed to customers will look like this: 





Relevant, fashion-forward product recommendations. A search for a brown, men’s dress shoe will give product recommendations that are within the same fashion category and style. 


As you can see, good things take time--and that includes product recommendation systems. While the YesPlz Product Recommendation System takes the time to study your products, the system can truly make the best possible recommendations for your customers.


You can try the Product Recommendation System here--and don’t forget to let us know your thoughts!

by Jess Erdman Jess Erdman
Content Marketing Lead

I'm passionate about creating cool content. The best part? I get to learn new things about fashion tech and ecommerce everyday. Have an idea or opinion about this article? Reach out at

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