Webshop Personalization has become an indispensable part of modern web shops. There are different types of recommendations that are placed in various areas of the web store. "Classic" recommendations usually appear on the product pages, often with captions such as "Customers who bought this product also bought" or "You may also be interested in." These recommendations usually come from the webshop solution itself and usually have a low conversion because they are less relevant to the visitor.
“Advanced recommendations” are those that are tailored to the behavior of the user and his / her possible purchase history. These recommendations are presented in, for example, a separate web shop section, “my shop environment” or in the order of products shown on a lister page, preset filters. Logging in and / or cookies make this possible. This form of Personalization does not offer the user general, but personalized suggestions. In addition to recommended products, personalized banners and promotions can also be displayed based on, for example, a search term typed in Google. Not only products, but also categories, catalogs, authors (in bookstores), etc. can be personalized. As the ultimate goal, Webshop Personalization solutions strive for a total personalization of the online store, which contains personalized navigation, advertisements, prices, mails and other messages.
Webshop Personalization technology is a lively research area. Hundreds of researchers are tirelessly devising new theories and methods for developing improved recommendation algorithms. The main goal of Personalization is an increase in the turnover of the webshop (or profit). So the challenge is to recommend products that are relevant to the user and will actually view and buy, while at the same time avoiding down-selling by recommending replacement, cheaper products.
Many webshops now have hundreds of thousands, often even millions, of different products in the range. From this gigantic quantity, we want to present the most suitable and most relevant recommendation to the customer. Moreover, the large number of special offers, changes in the range and prices create the situation that good recommendations are already out of date shortly after they have been learned. Good Personalization technology must therefore be able to act in a very dynamic environment. This gives us the main challenge of Personalization technology in sight, the ability to exhibit adaptive behavior.