The recommender system is one of the best technological developments in the world, Especially if you have an internet business and many users visit your website every day. Recommender systems provide relevant suggestions to users or customers based on predetermined algorithms, user interests, and search history; These offers certainly play an important role in increasing user interaction with the website and increasing sales.
What is the concept of a recommender system?
The recommender system concept is very simple; to explain it, it is better to start with an example. You must have visited many store websites by now. By entering the website, you will search for the product you want and may visit different web pages for that.
If the website of the desired store uses recommender systems technology, you will come across suggestions related to your search after browsing a few web pages. Maybe you’re looking to buy white headphones with a wireless feature. With a good recommender system, you will come across offers related to the same product, and you may even get products with the desired color.
In fact, with your favorite searches and predefined algorithms, the recommender system is being trained from the beginning and will provide relevant suggestions after properly understanding your interests and needs; Of course, the matter is not as simple as this and designing a powerful model of recommender systems requires a lot of precision and is a time-consuming task.
What is the use of recommender system or recommender systems?
The application of recommender systems, also known as recommender systems, is very wide. One of the best places to use these systems is store websites with many similar products. On these websites, you can make the best use of Recommender Systems, which is important in improving customer experience and increasing sales.
Of course, these types of systems can be used on websites with different products, and libraries are also part of the applications of these types of systems. Among these things, we can mention video-sharing websites, applications, and even music-sharing, which are also very popular among users. If you log into popular video-sharing services like YouTube and after a few minutes of browsing, you will see the role of a powerful recommender system equipped with machine learning and artificial intelligence technology.
The connection of recommender systems with artificial intelligence
Artificial intelligence is the main part of the story for implementing recommender system algorithms. Looking at the surrounding environment, we see many applications of artificial intelligence, and this technology is progressing day by day; In fact, Artificial Intelligence is a vast scientific field of which machine learning is a sub-branch.
In machine learning, you can use models with learning power, and in this process, you can use the input data to learn the models. With the success of the learning process, we will have trained models that can be used to get the desired outputs.
Using a machine learning model to create a recommender system is one of the best applications of artificial intelligence. In this model, you can apply inputs and go through the learning process. In this system, our input can be the user’s search type, product name, product weight, color, and any other criteria; After learning the recommender system model based on the set criteria, our future outputs, which are suggestions, will have the closest features to the user’s interests.
Does machine learning have a role in recommender system development?
Machine learning is a branch of artificial intelligence science that plays a fundamental role in processing large and collected data. When we talk about data, owners of large businesses such as online stores are the main audience; There is a lot of data in the user experience process that can easily be used to improve the business.
But our main problem is the multitude of data and its huge volume. In the meantime, applying the idea of machine learning and designing models that can be learned and can provide the desired output will be our best solution; Machine learning is based on designing powerful algorithms, applying inputs, and thus making decisions and providing outputs.
This process has a great speed, and if the models are designed accurately according to our needs, we will certainly have very close to ideal outputs; It is better to pay more attention to the practical examples of the application of machine learning and artificial intelligence in the surrounding world:
- Google self-driving cars
- Typing and automatic correction of texts
- Google search results algorithm
- Offer products on the Amazon website
Types of recommender system
In general, the types of recommender systems are divided into three parts, which will be explained below. We should use these models based on different conditions and the desired output and input type; Of course, some more models and algorithms differ in criteria and outputs.
Content-based recommender systems
You see a profile by browsing the different products on a bookstore website. This profile states the information related to the desired product and its content. In the content-based method, we use this product profile and use this content as a filter.
It should be said, if a user has visited a book and is interested in buying it, it is likely that the same is true for similar books; Therefore, by filtering content and grouping products based on similar features, good results can be achieved to present to the user.
Interactive or collaborative recommender systems
The interactive recommender system provides the desired suggestions for users with similar tastes based on the user’s interests. In this method, which is one of the best methods in reliable websites and services, product filtering is done interactively based on the type of user search.
There are two methods in this type of system: user-user or commodity-commodity. If you use online stores, you have seen that products with the title “Other users have also visited these products” are displayed to you. This method is called user-user. In this method, our criteria for selection are other users who have similar tastes to the current user.
In the product-product method, products with the title “You may also like these products” are suggested, which have the same characteristics as the product selected by the users. In fact, our main criterion is similar goods.
Hybrid or hybrid recommender systems
A hybrid model recommender system is actually a combination of previous methods and is used to provide optimal suggestions to the user. As mentioned, this method has excellent optimization of the final outputs and will play a fundamental role in improving the results.
In the interactive method, we can choose products based on the user’s taste. However, optimizing it in the second stage and with the content method is very important. All the products selected by the interactive method will not have the desired features and must be filtered in the second step; Finally, the outputs of hybrid recommender systems are displayed to the user more accurately.
Why do we need recommender systems?
There are several reasons to use a recommender system on a website. If we pay attention, users are always interested to see similar products in the same category and compare them. This is also true for video-sharing websites and video content.
By using optimal algorithms of recommender systems, this need of users can be answered easily. Using this method to improve user interaction and increase the content viewing rate has a great impact.
Of course, there is an important reason to encourage us to use machine learning and machine processing power. The huge amount of data comes from different channels and this data, while a lot, has a lot of value for our internet business.
How does a recommender system work?
Looking at the image below, you can see how a recommender system works, which will be implemented using computer codes.
In this method, the user-user model is used, and two users have similar tastes. The reason for this similarity of tastes is their search records, and finally, the content searched by the first user can be suggested to the second user as well. As you can see, this method is a simple concept that can be best used in a business and store website.
Based on the algorithms of the recommender system and based on our own goals, we can offer users products that fall into optimized categories with the following sentences:
- The site’s best-selling products
- Associated with the currently selected item
- Offer based on purchase history
Application of recommender system on different websites
Recommender systems are most commonly used in store websites, libraries, and CRM systems, where there are many choices. With the correct use of this technology, you can certainly see its results in increasing sales and increasing the rate of visiting website pages and products; For example, by searching for a gaming laptop or computer in online stores, you will probably see similar products such as gaming headsets, game consoles, gaming mice and keyboards, etc.
In addition to store websites, recommender systems can also be optimally used in educational websites. In these types of websites, users are very interested in reading similar content, and using content recommendation systems plays an important role in the growth of the website’s rank; For example, below this article, you will probably see several suggested titles based on similar topics.
Recommender system implementation tools
Recommender systems have helped a lot in the growth and development of new businesses and startups. Among these are Amazon, Walmart, YouTube, and big websites like Google.
In the world of technology, we have various tools to implement these types of systems, some of which are commercial and some of which are available to users in the form of open source; In the following, we introduce some powerful tools in this category.
LensKit recommender system tool
This tool is released as open source and is used for creating, researching, and developing recommender systems. This tool is provided for the Python programming language and is compatible with the famous libraries of this language, such as Scikit and TensorFlow.
Crab recommender system tool
This tool is also provided for the Python programming language and will work best with the powerful libraries of this language. There is a possibility of great configuration and numerous customizations in this tool.
TensorRec recommender system tool
This tool is specially designed to use the TensorFlow Python library, allowing great customization and algorithms at full speed. In this tool, we will have three types of inputs, which include user characteristics, product characteristics, and user interaction. This tool optimally uses data to learn and provide the best outputs.
Raccoon Engine recommender system tool
This tool works based on an interactive recommender system known as an NPM module. This tool requires Node.js and Redis, which can be used for various businesses and online stores because it is developed as an open source.
EasyRec recommender system tool
This tool is developed based on Java and is available to users as open source. RESTful web service is used in this tool, and this recommender system can be used embedded in web applications.
How far do you think recommender systems can penetrate our lives? Will there come a day when such a system will replace humans and provide us with all the suggestions we need? Please share your valuable opinions and views with us.