The algorithm looks for similarities between the specifications of two products and tells the user which ones are worth paying attention to. For example, if Martha buys the book Children of Bullerbyn based on the data contained in the description same author, same age category, same publisher, the recommendation system will tell her to also buy the book Pippi Longstocking Complementary filtering Method of indicating recommendations is an innovative way that allows the consumer to choose which set of recommendations he will view. Using a combination of the above techniques, this system provides the user with four sets of recommendations at a time.
By means of a small window adapted to the stores website, the user is given the opportunity to choose what will be recommended to him at a given moment. Thanks to this variety of recommendation models, the consumer not only has a wide range of personalized recommendations at their Vietnam WhatsApp Number List disposal, but also a simpler way to find the product they are interested in, without having to navigate the entire store page. Lets take a look at how these models work. Similar – a recommendation model created on the basis of collected data on products that have similar or the same elements in the description.
For example, the book Children of Bullerbyn and the book Pippi Longstocking will appear in the recommendation as similar products, because their specification on the website has several common elements category, author, publisher, translation author, original language. Popular – thanks to an algorithm that remembers user sessions and behavior, the recommendation system can suggest products of a given category that have been viewed most often. When Martha visits the Bullerbyn Children page she will see a set of recommendations consisting of the three books that other consumers have most viewed in the Childrens Literature category Others Ha