Product recommendation engine definition and types

ecommerce recommendation engine

Product recommendation is a strategic approach for the sellers to boost sales and increase customers’ satisfaction. However, there are various options and methods that the sellers need to choose the best-fitted for their stores. Our article today will go through the product recommendation engine, including definition, operation rules and categories.

A Quick Guide Of Product Recommendation Engine

What Is Product Recommendation Engine

Product recommendation engine is a tool that takes advantage of artificial intelligence (AI) and machine learning. It allows allow the business runners to come up with product suggestions and well-matched deals for each customer. The process of generating product recommendations requires accurate customer data. They are purchase history, time spent, order value and careful analysis to produce the exact offers.

How Does Product Recommendation Engine Work

Product recommendation engines are considered as a complicated process, which is based on analytic algorithms. It uses customer data for forming suggestions. Those are not only the on-site data but also the data from running paid-ads, forecasting demand and so on. Then, your system will deliver those offers to target customers.

Product Recommendation Engine’s Classification

Collaborative filtering

The first product recommendation type that we want to tell you is collaborative filtering systems. This approach will summarize and analyze various customers to identify the general trends and offer specific recommendations to typical customers. For instance, when the system detects that there are customers searching for a pair of trainers and viewing certain items. It will examine other customers who have viewed the same products and recommend the purchased items of previous buyers for current customers.

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Content-based filtering

Another product recommendation engine is content-based filtering systems. Which use the information from customers’ portfolio, preferences and purchasing behaviors to analyze and generate product suggestions. Based on customer information, the system will examine, summarize and create a specific profile for each customer. So that it enhances the accuracy of each product recommendation. You may come across this type of product recommendation frequently. It is shown as “Since you bought this, you may also like…” or “related items”.

Hybrid recommendation

The final product recommendation type is hybrid. It is the combination of two approaches above: collaborative filtering and content-based filtering systems. This hybrid product recommendation form can take advantage of both approaches above. So it can generate the most exact and helpful product recommendation. It bases on the groups of similar customers as well as unique preferences of each customer. The hybrid systems will analyze those methods separately and then combine and select some specific suggestions for customers. For example when a customer wants to buy a laptop for playing games. The system will summarize current customer’s preferences to generate the best well-matched suggestions.


You sell on the BigCommerce platform and find it difficult to apply the suitable product recommendation engine into your selling? Yyou may take advantage of Product Recommendation apps from GritGlobal. It helps to generate personalized, helpful, customized and accurate suggestions for your customers.


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