What are Product Recommendation Engines?

What are Product Recommendation Engines?

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Product Recommendation Engines are not unfamiliar to almost all online merchants. Such these engines are designed to maximize the effectiveness of selling activities online. If you are new to ecommerce market, this blog will give you an overview about functions and advantages of product recommendation engines.

Define products recommendation engines

If you often visit an online store and search for any item, you undoubtedly experience a chain of recommended products featured in the result page of your searching product for at least once. This list is definitely the work of product recommendation engines.

Product recommendation engines are filtering tools that automatically recommend a list of relevant products to every single user to increase their demands to purchase as many items as possible. In other words, these engines are automated to make the best of algorithms in search engines and data collected to analyze and figure out the most appropriate suggestions for each individual. By applying product recommendation engines, store’s consumers will receive a bundle of related products beside the results for the product they really want to look for. These listed suggestions are possibly also bought by customers or at least force web browsers to find more information about these items.

The appearance of possibly-purchase products is convenient for both store owners and online buyers. While shop admins will have chances to introduce more products, shopping doers do not need to waste their time finding these products that match their interests and tastes.

Classify different types of recommendation engines

Basically, there are three types of recommendation engines used to generate recommendation for every online customer, which are collaborative, content-based, and hybrid recommendation systems.

Collaborative filtering:

This approach of recommendation engines makes use of information collected through shopping behaviors of customers such as kind of product they look for, their interest, and their preference. All information will be gathered and analyzed to classify consumers into a particular group. As a result, a list of might-like products will be predicted and sent to web browsers, so that they can easily take into consideration.

Content-based filtering:

This method focuses on customer’s data collected about their profile and products they already purchased. In other words, keywords are used to analyze the preference of consumer as people search for their preferred products via keywords. It means that, the more you visit and purchase goods at an online store, the better suggestions they can offer to you. Hence, a series of similar products will be presented based on previous preference and shopping activities. However, there is a detrimental issue with this kind of filtering. Since all these recommendations are based on analyzing recorded information, if a buyer first drops in a store, the suggestions they receive possibly do not match their preference.

Hybrid Recommender Systems

This approach in short is the combination of two methods above: collaborative filtering and content-based filtering. These two methods process separately then their results will be combined to optimize the most appropriate answers for the question: what items do customers really desire to look for? As this method has all advantages from two components, it is considered the best choice for product recommendation engines.

In conclusion, along with marketing strategy, online merchant owners should pay attention to serve every customer at best to make them feel being treated in a special way. Therefore, it is necessary for shop admins to have a supporting tool such as product recommendation engines. If you are looking for an extension which can meet all requirements for a good product recommendation engine, Mageplaza automatic related product extension is here waiting for you to experience its effectiveness.