Do We Really Need Machine Learning for Personalized Recommendation System? (Spoiler: Yes)

How Does a Recommender System Work

Recommendation systems using machine learning are used for both scientific and business purposes. In business, such systems are designed to give the customer information about the service or the item that they might be interested in. Depending on the business type, the machine learning algorithms used for recommendation systems can serve different functions: propose movies and shows, new clothes and goods, restaurants, etc.

The base of every recommender engine system is a table, where the rows contain the users of this system and the columns contain the items that are available in the system.

At the crossing point of the user with the item, there is a box with a rating. This rating shows the user’s level of interest in the item. The problem is, many users rarely rate the items that are available in the system. Because of that, the result table turns out to be very loose and mostly empty.

This way, a recommendation system has to combine the available data and consolidate the missing values by the assumed rating. In addition, not all services offer the opportunity to rate the available services or products, which complicates the task.

Considering the reasons stated above, the selection of users’ rates in the system can be done in a few ways: explicit and implicit.

The Difference Between the Explicit and Implicit Rating Methods

The first method implicates the univocal rating of an item by a user, thus, displaying the obvious interest in a certain product/service. A recommendation system example would be rating a product with a five-point score.

The second, implicit method implies the rating of the user’s actions by a system: i.e. estimating the number of purchases of this specific item or estimating the time of watching a certain video. This approach is the most popular and is almost all-purpose. It works well both independently and in conjunction with the explicit method to make the latter one more precise.

When comparing the two methods, the implicit one is simpler in terms of both data analysis and implementation. This is related to the fact that with an implicit approach, we can only make an assumption on whether the user liked a product or not.

For example, the view of a video does not always mean a person is interested in it and a few purchased products would not guarantee the interest either as they could just be a gift for another person.

Recommendation System Types and Their Features

Depending on the architecture of a software product, data structure, and the analysis method, there are a few types of recommendation systems. The biggest ones are:

• non-personalized;
• collaborative filtering;
• mixed.

Non-Personalized Recommendation Systems

This is the simplest system of all the available algorithms. The systems working by it recommend the product by the following principle: if everyone likes it, you will like it as well.

Such an approach is not very precise and the results are quite questionable and not precise at all, as the system often shows completely irrelevant items. This algorithm is suitable for the systems that do not require user registration or that are used in addition to the other algorithm. Non-personalized recommendations are mostly used to provide recommendations for first-time users when the system knows nothing about their preferences yet.

Collaborative Filtering

This is the most popular system, which began developing in the 90ies. With collaborative filtering, recommendations are calculated on the basis of related users or items.

The results are achieved by a cooperative comparison of multiple users and turn out to be very precise and accurate.

Even though the use of this method delivers very good results, its practical realization is quite complex due to the big number of calculations that are needed to search for similar users. This is related to the high asymptotic complexity of the algorithm which is an indicator of the algorithm’s efficiency. This, in turn, leads to the rapid growth of memory consumption and computational power. Such an approach is not very stable in relation to the dynamic systems and can be used for static systems only, like looking for food matching.

Luckily, there is a quicker algorithm to implement ?  mathematical model, which became really popular in recent years. This algorithm is matrix factorization. This approach is based on the theorem that any matrix can be displayed by the composition of two others. In the

ML recommendation systems would be the user matrix and the object matrix. Each of these matrices displays the special aspects of the users or the objects. Such an approach is considered incredibly efficient as it can precisely identify the “hidden” wishes of the users and delivers really precise results. For now, matrix factorization is one of the most efficient ways to implement recommendations.

Mixed Recommendations

In practice, the use of one algorithm for creating a deep learning recommendation system will not deliver the needed result so it’s better to use mixed recommendations to solve problems that are related to a certain algorithm type.

Any system that is based on Machine Learning principles demands initial data to teach the weighting factors. With the lack or absence of the data, the system will not be able to deliver results. In recommendation systems, this problem is known under the “cold start” name. Each algorithm requires a certain set of data, needed for proper work. The problem appears upon launching the service in production when the number of users is minimal and the used algorithms are not applicable.  There are a few solutions to the problem. First, the items can be recommended by the date of adding to the cart or randomly choosing from the whole item set, hoping the user will like some of them. Second, the system can be started after the service works for a while and there will be enough data to analyze.

Conclusion

Machine Learning is a powerful tool for solving numerous tasks. Such features as personalized recommendations, associations, and classifications of various items can become a valuable asset for almost any system. Our IT consulting services help you to properly implement any kind of ML to empower your business. In future articles, we will have a closer look at Machine Learning and specifically on the recommendation system with implementation on the Python development services with NumPy and scikit-learn libraries, their working principles, and possible ways of implementing them for real-life projects.