Technologies of recommendation systems

When watching a movie or buying a product on online platforms, have you ever wondered what is behind the suggestions that are automatically offered to you? This mechanism is based on recommendation systems. The recommendation systems make it possible to offer consumers products that may be of interest in order to improve purchasing processes and enable up- and cross-selling. A recommendation mechanism is a tool that uses a series of algorithms, data analysis and even artificial intelligence (AI) to make online recommendations.

Recommendation systems are a type of content filtering system. They can be described as algorithms that suggest articles that may be of interest to the user of a website or application. This type of algorithm is used in various areas. The most obvious examples are e-commerce services (e.g. Amazon), streaming services for movies, videos or music (e.g. Netflix, YouTube, Spotify), social platforms (e.g. Instagram), delivery services (e.g. Uber Eats) and so on. So wherever there is the possibility to suggest content to a user, a recommendation system can be used to make this content user-specific. According to a survey by BrightLocal, 88% of users trust online reviews as much as personal recommendations.

How do recommendation systems work?

A classic recommendation system processes data in these four phases: Collecting, storing, analyzing and filtering.

• Collect

Data collection is the first step in creating a recommendation system. In reality, the data is classified into explicit and implicit. The information provided by users, such as ratings and comments, is explicit. On the other hand, implicit data consists of search history, order and return history, clicks, page views and shopping cart actions. This information is collected for each user who visits the website. Collecting behavioral data is difficult because the activities on the website must be stored, and this information is different.

• Save

You have to decide which type of storage is required. For example, you can use a NoSQL database or the SQL standard. An expandable and manageable database reduces the number of tasks required to a minimum and focuses on the recommendation itself.

• Analyze

Special analysis methods are used to analyze the collected data.

• Filter

The next phase is to filter the information to offer relevant recommendations to users. To implement this method, you need to select an algorithm that is suitable for the recommendation system you are using.

Types of recommendation systems

Depending on whether you start from user wishes or the similarity of products or elements, the recommendation systems can be divided into: collaborative filtering, content-based approaches and hybrid models. All are based on the preferences of users in the past.

Kollaboratives Filtern

This is the first approach with the aim of developing a proposal based on the preferences expressed by users, even without taking into account the characteristics of the products. A user is advised on the items that they have not yet rated, but which have already been positively rated by other users with the same interests.

The system identifies two users who have similar tastes because they have listened to the same songs. If one of them hears a song at a certain time that the other does not know, this could be suggested to them.

Amazon is a typical example of collaborative filtering. They use collaborative filtering techniques that are scalable from element to element to create high-quality recommendations in real time. Amazon allows customers to easily compare similar items on product detail pages, create product recommendations, recommend products on category pages and much more.

Collaborative filtering can work well on websites where there is not much information about the elements and the content is difficult for a computer system to analyze, such as opinions. It can also recommend the goods that are relevant to a user, even if the content is not in that user's profile.

The main advantage of collaborative filtering methods is that they do not need to extract information about users or articles, so they can be used in different contexts. The more users interact with the products, the more information is available and the more accurate the new recommendations will be.

Their disadvantage is that when you have new users or new articles, there is no information about their interactions in the past. This situation is known as the "cold start problem". In this case, different techniques are used to determine which product recommendations should be shown. The system cannot show any recommendations or make weak predictions. For example, randomly selected items can be recommended to new users, or new items can be recommended to randomly selected users, popular items to new users or new items to active users.

Content-based filtering

The content-based filter analyzes the attributes of the elements to generate predictions. The recommendations are generated based on attributes extracted from the content of elements that a user has interacted with in the past. The user is recommended items that mainly relate to the top-rated items.

This is a semantic method, which is characterized by:

• Calculation of the similarity between new products and previously positively rated products by the common characteristics.
• For news or movies, since it is a text, the occurrence of keywords is referenced to find other similar elements.

This implies tagging the text, which consists of linking each product with keywords that can then be used to characterize it in the research phase.

The advantage of content-based methods is that they do not have the cold start problem because new users and new articles are defined by their characteristics and recommendations. The disadvantage of this approach is that the data (recommendations) may not be accurate, and the implementation of this recommendation system may take more time.

Hybrid model

The previous approaches are characterized by positive and negative aspects, based on the different needs each platform chooses which one is relevant for them. In general, the hybrid model is the most commonly used, precisely because it combines the two approaches to achieve better system optimization. This avoids the limitations and problems of pure recommendation systems. For new users, for example, the content-based system is more appropriate, while the collaborative filter is applied when certain information is already known about the users.

Such a combination of algorithms provides more accurate and effective recommendations than a single algorithm, as the disadvantages of one algorithm can be overcome by another algorithm.

The recommendation systems create tailor-made options and offer personalized experiences in today's world. Currently, 35% of Amazon's sales are generated by recommendation engines. Also, 75% of what people watch on Netflix comes from their recommendation system. As such systems grow in popularity, they should be a top priority for companies that want to be competitive and efficient for their customers. By using referral systems, you will increase your sales and improve customer satisfaction and loyalty.

KennerSoft GmbH](https://kennersoft.de/) already has several recommendation algorithms and frameworks to implement the recommendation systems for you. However, you need to analyze your data to determine which combination of algorithms is best suited for your business. We also offer programming, website optimization and Google SEO optimization services.

We would be happy to advise you on this topic, please contact us.