Introduction to Product Recommendation Engines

Posted by Ruban Corbo - February 15, 2013 - SEM - 8 Comments
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For those adept at internet marketing, you may have heard of product recommendation engines before. For those who haven’t, you’ve likely stumbled upon one in your internet browsing experience before and weren’t aware of it.

What is a Product Recommendation Engine?

A product recommendation engine takes into account the user’s previous internet browsing history and display a list of relevant products to them on their homepages. Two key players in this field are Amazon and Ebay – visit these sites and you’ll notice right away custom-tailored products delivered to the homepage for your viewing.  The entire product and service database of their website is accessed by product recommendation engines.

Product recommendation engines take into account not only the description of the product or service but information it can obtain from your own social environment and previous web history.

How Does a Product Recommendation Engine Work?

To come up with accurate results for a website’s potential customers, product recommendation engines take into account the following approaches:

Collaborative Filtering

With this approach, a product recommendation engine first gains access to a pool of users and collects data based on their behavior online, their activities, and their preferences. All the information collected is then filtered and submitted to a platform which categorizes them into products that a group of users may like or dislike. Upon entering the site, this recommendation method determines which group of users you belong to and provide recommendations on the assumption that your tastes are similar to users it had studied in the past.

Content-based Filtering

Activities, browsing history, and preferences attributed to you alone are considered with this type of approach. The more time you spend browsing the site, the more effective this approach will be as the product recommendation engine has little to base its recommendations on during the first few times you visit the site. You’ll find the first few visits produce results that aren’t as accurate the longer your visiting the site.

Hybrid Recommender Systems

The hybrid recommender system provides the best of the two aforementioned strategies, which many consider make it the best out the three approaches. Because the web behavior, activities, and preferences of similar users as well as those attributed to the actual target customer are both considered, the product search engine has more information to work with. The result is having a much better chance of improving the accuracy and validity of its predictions.

If you haven’t already, it might be a good decision to install a product recommendation in your own shopping website. A good example of the breath of work these engines provide is on Amazon, which utilizes a product recommendation engine to come up with different types of recommendations such as “Related Items” or “Items to Consider”. A list of “previously viewed items” is even included, which shows how you can apply a variety of predictions or recommended products to your own site.

About the Author

Ruban Corbo

This guest post is contributed by Ruben Corbo, a freelance writer that writes about technology, gaming, music, and online marketing where you can read more about product recommendation engines. You can read more about this particular subject at Maxymiser.com as well as traffic to sales converting topics. When Ruben is not writing, he is composing or producing music for short films and other visual arts.