With every day developing online businesses and stores, the usage of recommendation engines has become a significant part of the e-commerce industry. All of these Netflix “Other Movies You May Enjoy” and “Customer who bought this item also bought …” on Amazon, Facebook “People you may know” are the best practices of recommendation system usage.

A recommendation engine or a recommender system is a tool used by developers to foresee the users’ choices in a huge list of suggested items.

Generally, algorithms developed for recommendation systems rely on purchases and page views done before. What is more, today there are many services suggesting in-the-moment recommendations, as they utilize artificial intelligence for analyzing interactions of the users and find visually proper products that will interest any individual customer. Due to AI, recommendation engines make quick and to-the-point recommendations tailored to each customer’s needs and preferences.

With the usage of artificial intelligence, online searching is improving as well, since it makes recommendations related to the user’s visual preferences rather than product descriptions.
Seemingly, artificial intelligence recommendation engines may become the alternatives of search fields since they help users find items or content that they may not find in another way. That’s is why today recommendation engines play an essential role for sites like Amazon, Facebook, YouTube and so on.

Let’s dig deeper and understand the working methods of recommendation systems, and see how they collect data and make recommendations.

Understanding basics of Recommendation System

A recommendation engine is an information filtering system uploading information tailored to users’ interests, preferences, or behavioral history on an item. It is able to predict a specific user’s preference on an item based on their profile.
With the use of product recommendation systems, the customers are able to find the items they are looking for easily and quickly. A few recommendation systems are developed so that to find products the user has watched, bought or somehow interacted with in the past.

The recommendation engine is a splendid marketing tool particularly for e-commerce and is also useful for increasing profits, sales and revenues in general. That’s why personalized product recommendations are so widely used in the retail industry, once more highlighting the importance of recommendation engines in the e-commerce industry.

For a recommendation system to be useful, it should be flexible to new user behavior. It should be able to act in a dynamic environment, providing the users timely information about special offers, changes in the assortments and prices.

Machine Learning

Examples of Recommendation Engine Usage

With the fast-growing quantity of information on the Internet and considerably high number of customers, it is crucial for companies to scan, search, filter and provide useful information to the customers according to their needs and tastes.

A vivid example of recommendation engine usage is by Amazon; with its “Customer who bought this item also bought…”. Generally, the content recommendation engine is like a clever and experienced salesperson who grasps the needs, tastes, and requirements of the user and is capable to make knowledgeable decisions about recommendations beneficial and relevant to the client’s wants, meanwhile increasing the conversion rate.

The statistics show that almost 35% of Amazon’s revenue comes from the usage of recommendation engines.

Thus, what is the strategy they use?

Amazon keeps the corresponding items before the eyes of the customers by using their browsing history. It provides the recommended and best-selling option based on the usage of customer reviews and ratings. Actually, Amazon is inclined to sell you a package instead of a product. Let’s pretend you have bought an earring, then it will suggest you corresponding necklace and bracelet. Afterwards, it uses a recommendation engine to email and keeps you noticed about the new trends of that category.

Amazon also uses recommendations for targeted marketing via email campaigns and website pages. Hence, Amazon starts recommending a lot of products from different categories based on your browsing history and picks up those items that you are probably going to buy.
Recommendation engines started their journey in e-commerce, nonetheless, they are gaining more and more popularity in other spheres as well, such as in the Media.

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A good example of recommendation engine usage in Media is done by YouTube and Netflix.
YouTube with its “Recommended Videos” and “Other Movies You May Enjoy” by Netflix are vivid examples of AI recommendation engine usage.

Netflix usually uses hybrid recommender systems. It starts by comparing the searching and viewing habits of users with the same interests.
Recommendation engines are becoming more and more widespread in the sphere of transportation industry too.

How Does Recommendation Systems Work?

Shopping has been, is and will continue being a necessity for humanity. It’s not a long time since we asked our friends for a recommendation for buying this or that product. Hence, it’s the essence of human beings to buy items recommended by our friends, whom we trust more. The digital age has taken into consideration this ancient habit. Therefore, any online shop you visit today, you may see some recommendation engine used.

With the usage of algorithms and data, recommendation engines filter and recommend the most relevant products to a specific user. As they say, it’s like an automated shop assistant. When asking for something, he also suggests another one that you may be interested in.
Developing product recommendation algorithm models is a research area that grows hour by hour.

Machine Learning in Recommendation Systems

In order to provide customers with service or product recommendations, recommendation engines use algorithms. Lately, these engines have started using machine learning algorithms making the predicting process of items more accurate. Based on the data received from recommendation systems, the algorithms change.

Machine learning algorithms for recommendation systems are generally divided into two categories; collaborative and content-based filtering. However, modern recommendation systems combine both of them.
Content-based filtering considers the similarity of product attributes and collaborative methods count similarity from customers’ interactions.
Generally, the core of machine learning is to develop a function predicting the utility of items to one another.

With so much information on the Internet and so many people out there using it, it has become of vital importance for organizations to search and provide data to their customers corresponding to their needs and tastes.

Recommendation engine processes data in four phases

A classic recommender system processes data through these four steps: collecting, storing, analyzing and filtering.

1. Collecting the data

Data gathering is the first phase of creating a recommendation engine. In reality, data is classified into explicit and implicit ones. Data provided by users, like ratings and comments are explicit. Whereas, implicit data may consist of a search log, order and return history, clicks, page views, and cart events. This sort of data is collected from any users who visit the given website.

Collecting behavioral data is not difficult, since you may keep a user activities log on your website. As each user likes or dislikes various items, their datasets are different. During some time, when the recommender engine is feed with more data, it becomes more clever
And the recommendations become more relevant too, so the visitors are more inclined to click and buy.

2. Storing the data

To have better recommendations, you should create more data for the algorithms you use. It means that you can turn any recommender project into a great data project quickly. You can decide the type of storage necessary for you with the help of the data you use for creating recommendations. It is up to you whether to use a NoSQL database or a standard SQL database or even some sort of object storage. All of these variants are practical and conditioned with whether you capture user behavior or input. A scalable and managed database decreases the number of required tasks to minimal and focuses on the recommendation itself.

3. Analyzing the data

In order to find items with similar user engagement data, it is necessary to filter it with the use of various analyzing methods. Sometimes it is necessary to provide recommendations immediately when the user is viewing the item, so here a faster type of analysis is required. Some of the ways for analyzing such kind of data are as follows:

· Real-time system
In case you need to provide fast and split-second recommendations you should use the real-time system. It is able to process data as soon as it is created. The real-time system generally includes tools being able processing and analyzing event streams.

· Near-real-time analysis
The best analyzing method of recommendations during the same browsing session is the near-real-time system. It is capable of gathering quick data and refreshing the analytics per few minutes or seconds.

· Batch analysis
This method is more convenient for sending an e-mail at a later date since it processes the data periodically. This kind of approach suggests that you need to create a considerable amount of data to make the proper analysis like daily sales volume.

4. Filtering the data

The next phase is filtering the data for providing relevant recommendations to the users. For implementing this method, you should choose an algorithm suitable for the recommendation engine you use. There are a few types of filtering, such as:

· Content-based
The focus of content-based filtering is a specific shopper. The algorithms follow actions like visited pages, spent time on various categories, items clicked on and etc. And the software is developed based on the description of the products the user likes. Afterwards, the recommendations are created based on the comparison of user profiles and product catalogs.

· Cluster
Cluster analysis is intended for finding smaller groups of cases. It tries to group cases more similar to one another in contrast to other types of cases. In this respect, recommended items fit each other regardless what other users have watched or liked.

· Collaborative
It makes predictions conditioned with the tastes and preferences of the customer and allows you making product attributes. The essence of collaborative filtering is the following; two users who have liked the same item before will like the similar one in the future.

Conclusion

In conclusion, it is beyond doubt that the recommendation engine as a service has gained more popularity and plays a significant role in the new digital era. In order to be competitive in the market and obtain more efficient customers using recommendation engines is in your best interests.
Especially with the use of artificial intelligence, in-the-moment recommendations are more widespread, which is time-efficient and pragmatic. Thanks to artificial intelligence, the recommendation engines have improved their productivity and they are based on the customer’s visual preferences rather than on the description of the items.