Recommendations appear everywhere. Netflix shows you movies you may like based on your watching patterns. And when you online shop, you may see suggestions like , “customers that bought this item, also bought this one,” or you see people you may know in social media platforms. These are examples of a type of machine learning called recommender systems.

Machine learning applies mathematical algorithms and statistical techniques on large amounts of data to create models that can generate predictions, recommendations, and detect anomalies. Recommender systems are one of the most successful and widespread applications of machine learning in business.

How does a Recommender System work?

How does a recommender system work? This process starts in the data collection phase where data can be classified either as explicit or implicit. Data provided by users, such as ratings and comments, are explicit. Implicit data consists of historical orders, returns, search logs, clicks, cart contents, and page visits.

This process starts in the data collection phase where data can be classified either as explicit or implicit. Data provided by users, such as ratings and comments, are explicit. Implicit data consists of historical orders, returns, search logs, clicks, cart contents, and page visits.

The training phase applies mathematical algorithms and statistical analysis on the collected data to “learn” the patterns that are present. The algorithms used in machine learning models for recommender systems can differ based on a specific problem. Let’s list the most common methods in order to understand them.

Collaborative filtering consists of recommending items that people with a similar taste have preferred in the past. For example, collaborative filtering analyzes the data to find two users who liked the same item previously and may potentially like a similar item in the future. This method can avoid repetitive recommendations but requires a lot of data.

Collaborative filtering consists of recommending items that people with a similar taste have preferred in the past. For example, collaborative filtering analyzes the data to find two users who liked the same item previously and may potentially like a similar item in the future. This method can avoid repetitive recommendations but requires a lot of data.

Image source: Ankur, Tomar, Medium

 

Content-based filtering focuses on the preferences of a specific user. The algorithms track actions such as pages visited, time spent in different categories, clicked items, purchased products, etc. Then, based on those past events, the system makes personalized recommendations comparing similar categories/labels. It is a simple method not requiring a large amount of information to suggest items, but it can offer repetitive recommendations related to the same class.

Content-based filtering focuses on the preferences of a specific user. The algorithms track actions such as pages visited, time spent in different categories, clicked items, purchased products, etc. Then, based on those past events, the system makes personalized recommendations comparing similar categories/labels. It is a simple method not requiring a large amount of information to suggest items, but it can offer repetitive recommendations related to the same class.

When collaborative filtering and content-based filtering are combined into a hybrid mode, they can balance each other’s strengths and weaknesses. Recommendation lists can be made by joining the indicators given by the two systems, allowing the initial user to receive recommendations at first, and users who have been on the platform for longer to not be restricted to a similar content bubble. A hybrid system can have separate lists like most viewed content by other users, user recommendations similar to a first-time user, and content that most resemble what a user consumed before.

After the machine learning model is trained, the recommendation data is ready to be consumed. A common approach is to deploy a model into a container that exposes a REST API, so that a web app can query the recommendation system with parameters based on the current user. The system returns recommendations to the web app for that user and it can display them on the page.

Machine learning systems are an iterative process. As you accumulate more data, the recommendation engine should be retrained so that it becomes smarter and improves its recommendations, making them more relevant and effective in getting visitors to click and buy.

 

Machine learning systems, like recommender systems, are an iterative process. As you accumulate more data, the recommendation engine should be retrained so that it becomes smarter and improves its recommendations, making them more relevant and effective in getting visitors to click and buy.

Machine learning-based recommendation systems have gained popularity and play a significant role in the new digital age. Microsoft Azure has various machine learning services that can be implemented in a very cost-effective manner and a timely fashion.

How would you incorporate a recommendation system into your web site or app? Imaginet can help you build and use these systems effectively. Contact us today to schedule a free consultation call to get started.

 

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Bruno Illipronti

About Bruno Illipronti

Bruno Illipronti is a Business Intelligence Developer at Imaginet. He has more than six years of experience in data development and worked on several projects supporting business initiatives and data-driven decisions utilizing analytic skills and data engineering techniques.

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