When watching a film or purchasing a product on online platforms, have you ever wondered what is hidden 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 might be of the interest, in order to improve purchasing processes and enable up- and cross-selling. A recommendation mechanism is a tool that uses a range of algorithms, data analysis, and even artificial intelligence (AI) to make recommendations online.
Recommendation systems are a type of content filtering system. They can be described as algorithms that suggest to the user of a website or application the articles that may be of interest to them. These types of algorithms are used in various fields. The most obvious examples are e-commerce services (e.g. Amazon), streaming services for films, videos or music (e.g. Netflix, YouTube, Spotify), social platforms (e.g. Instagram), delivery services (e.g. Uber Eats ) and so forth. So wherever it is possible to propose 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 well as personal recommendations.
A classic recommendation system processes data in these four phases: collecting, storing, analysing and filtering.
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 behavioural data is difficult because of the need to store activity on the website and this information is different.
One has to decide what kind 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.
Special analysis methods are used to analyse the collected data.
The next phase is to filter the information to offer relevant recommendations to users. To implement this method, it is necessary to choose an algorithm that is appropriate for the recommendation system you are using.
Depending on whether you are assuming 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 relate to the preferences of users in the past.
This is the first approach with the aim of coming up with 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 articles that he has not yet rated, but which have already been rated positively by other users with the same interests.
The system identifies two users who have similar tastes because they heard the same songs. If at some point one of them hears a song that the other does not know, it 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 produce high quality recommendations in real time. Amazon enables customers to easily compare similar items on product detail pages, make product recommendations, recommend products on category pages, and much more.
Collaborative filtering can work well on websites that don't have much information about the items and the contents of which are difficult for a computer system to analyse, such as: B. Opinions. The goods that are relevant for a user can also be recommended, even if the content is not in the profile of this user.
The main advantage of collaborative filtering methods is that they do not have to subtract any information about users or articles so that they can be used in different contexts. The more users interact with the products, the more information is available and the more precise the new recommendations will be.
Their disadvantage is that when you have new users or new articles, there is no information about their past interactions. This situation is known as a "cold start problem". In this case, various techniques are used to determine which product recommendations to show. The system cannot make recommendations or make poor predictions. For example, randomly selected articles can be recommended to new users, new articles can be recommended to randomly selected users, popular articles to new users, or new articles to active users.
The content-based filter analyses the attributes of the elements to generate predictions. The recommendations are generated based on attributes extracted from the content of items that a user has interacted with in the past. Articles are recommended to the user, mostly related to the top rated articles.
This is a semantic method, which is characterized by:using
This implies a tagging of the text, which consists in linking each product with key words, by means of which it can then be characterized in the research phase.
The advantage of content-based methods is that they don't 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 exact, and implementing this recommendation system can take more time.
The previous approaches are characterized by positive and negative aspects; based on the different needs, each platform selects which is relevant for it. As a rule, the hybrid model is used most often precisely because it combines the two approaches in order to achieve better system optimization. This avoids the limitations and problems of pure recommendation systems. For example, the content-based system is more suitable for new users, while the collaborative filter is used when certain information about the user is already known.
Such a combination of algorithms provides more accurate and effective recommendations than a single algorithm, since 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 through recommendation engines. Plus, 75% of what people watch on Netflix comes from their recommendation system. As such systems become more popular, they should be a top priority for companies that want to be competitive and efficient for their customers. Using recommendation systems is a highly efficient way to increase your sales and improve customer satisfaction and loyalty.
Kenner Soft Service GmbH already has several recommendation algorithms and frameworks to implement the recommendation systems for you. However, one needs to consider available data to determine which combination of algorithms is best for your business. We also offer programming, website optimization and Google SEO optimization services.
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