E-commerce sites continue to evolve by adding new features day by day. In order to provide customers with better experiences, in addition to detailed filtering systems, referral systems that offer recommendations based on purchases made are also being developed. The main topic of this article is about the content-based recommendation system, but also briefly about referral systems in general...
Referral systems have been developed to provide efficient, convenient and personalized service to the user. Its most important feature is that it can predict preferences by analyzing the behavior of others, while offering personalized advice to one user.
In order to make predictions, attention is paid to the preferences and behavior of users in the past. Referral systems are often encountered on e-commerce platforms, but their uses are increasingly numerous. Collaborative filtering, content-based filtering or hybrid methods can be used in its formation.
To understand the collaborative filtering method, let's consider two imaginary users (user a and user b). Let A and B make similar choices. Offering a product that A has bought but that b has never seen as a recommendation to b is done through collaborative filtering.
Content-based filtering goes into a bit more detail and uses information from item contents to predict items that users might be interested in.
Hybrid methods, on the other hand, are a combination of collaborative filtering and content-based filtering methods. Both methods have disadvantages, hybrid methods have been developed to eliminate them. Since these methods began to develop, many data scientists have come up with a solution of their own. We will talk about these methods in other articles, as these methods will be excluded from the topic of this article.
Although content-based filtering approaches require additional information about products and user preferences, there is no need for a database of user community or review scores. If you wish, let's explain content-based filtering with an example:
A user who is a member of the movie platform in the classic recommendation method determines the genres he likes and is offered recommendations based on his preferences. So if he says he likes science fiction film, recommendations are made from among popular science fiction films. When using a content-based recommendation system, the same user can be recommended movies that were shot in the same year as the movies they watched, or featuring similar actors, instead of recommending all science fiction films.
Similar scenarios are also used today to suggest a product on an e-commerce site, a blog post on a content platform, or new songs in a music app.
Let's talk a little about the advantages and disadvantages of this method.
— For the method to work better, codes must be written by hand, it does not have a ready-made template. Therefore, it requires advanced coding knowledge.
— Recommendations are limited only to the interests that the user has identified (told us). The recommendations that can be made with this method are therefore limited.
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