What is Recommendation Engine?

Digital channels, which are becoming more and more widespread, offer services that enable users to perform many activities from watching movies to researching or playing games to shopping. The widespread use of digital channels has caused many brands to sell their products and services through these channels, and a serious competitive environment has emerged in this field. In this competitive market, brands need to offer the right products and services to the right customer group in order to develop a successful sales strategy.

The recommendation engine, which is one of the most important methods for the sales of brands, is a data filtering tool used to suggest relevant products and services for different user and customer groups. At this point, the main goal is to use the machine learning algorithms of the recommendation engine to collect data for users on their interests. To explain with a few simple examples, it can be said that by collecting data for users with the recommendation engines of movie and TV series platforms, other TV series and movies can be suggested for their interests. Or, within this category, we can give other product recommendations for customer shopping analysis on e-commerce sites that may be of interest again.

What Are the Types of Recommendation Engines?

There are three different recommendation engines which help to provide important recommendation data for the users or customers.

• Collaborative Filtering: Collaborative filtering is applied by following behavioral analysis by comparing the similarities of certain group users with other group users. The main purpose here is to provide the right product and service to users by making inferences about what they will like or not like by using their interactions and behavioral analysis. The basic logic here is to make inferences based on the past preferences of the users and as a result, to offer a similar product and service variety for the future. The biggest advantage of this filtering method for brands is that it can be applied much easier because it focuses on personal predictions rather than content in this analysis process, and accordingly, it can suggest various products and services in a different way.
• Content-Based Filtering: The content-based filtering system is a slightly more complex recommendation engine; this is because multiple factors need to be evaluated and analyzed. In this filtering method, the main focuses are on the type, color, word length and description of the product, in addition to user behavior. Cosine and Euclidean distances are used to compare product similarities and present them to other relevant users. The point that is generally considered as a disadvantage here is that this filtering method recommends the products that the users have already purchased and those of the same type. For example, if a user has purchased kitchenware, this filtering method can only recommend the same or similar product to that user. Therefore, it can be said that its area is more limited than other filtering methods.
• Hybrid Model: Research over the last few years reveals that much more efficient results are achieved when collaborative and content-based recommendation filters are combined. In the hybrid model, these two separate filtering methods are applied sequentially, and then special recommendations for users are applied by combining them. Since both filtering systems are used in this method, problems such as data insufficiency are eliminated, and a much more efficient system is created.

Recommendation engines are very important in digital channels in order to advance the digital services and sales processes that offer them in various forms for users and customers more efficiently and to sustain the increase in sales rates.

Developed to customize the user and customer experience, Recommaster works within the framework of a system to increase the number of members and sales rates on digital platforms. These customizations help users and customers interact more frequently by increasing their trust and interest in the platform, thus increasing their traffic to the platform on websites or mobile.

Another reason why Recommaster is preferred by many brands is that it has a structure that can be customized not only on the basis of users, but also based on brands, and can offer the opportunity to add new features. Recommaster, which provides many opportunities and convenience for brands that want to use different features in digital channels and are open to innovation, stands out with the features it offers to both users and brands. With the advanced technology provided by Recommaster, you can create an enjoyable experience for users and increase your website or mobile channel traffic with digital channels that can be easily developed and customized.

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