The approach is based on a model of user behavior and the similarity of user preferences, where a group of users with similar interests is formed based on various characteristics. The idea behind this approach is that users in this group will be recommended items that were of interest to other users in that group. These systems use a degree of similarity. If users select similar items, then they are considered similar too. The similarity in tastes of two users is calculated based on the similarity of their rating history.
Content filtering is based on the similarity of elements that were previously interesting to the user in the past. The main idea is to compile a new list of similar elements based on the elements previously used by the user and offer them to the user. For example, if a user has rated an article positively on some topic, then the system can recommend other articles on a similar topic. A separate task within the framework of this approach can be considered the need to isolate features from elements, on the basis of which the degree of their similarity will be considered.
Recommendations are based on knowledge of the subject area (and not about each product). This type of recommendation is highly accurate, offering the user exactly what he wants. In addition, the system studies and analyzes the relationships between objects, takes into account a number of additional options related to the individual properties of a particular user. These properties include user wishes and demographic characteristics (initial data used by major social networks such as Facebook, LinkedIn, and others). The main disadvantage is the complexity of development and data collection.
Recommendations are based on a combination of collaborative and content approaches, which avoids most of the shortcomings inherent in each system. The following types of combinations are present in hybrid recommendation systems: implementing separately collaborative and content algorithms and combining their assumptions; the inclusion of some content rules in the collaborative methodology; the inclusion of some collaborative rules in the content methodology; building a general model that includes the rules of both methods.
The main disadvantage is the complexity of development.
Explicit data collection in recommendation systems
If there is an explicit user’s data collection, it is necessary to receive completed questionnaires to identify preferences. The disadvantage of this method is that it is quite difficult to get the user to rate.
Implicit data collection in recommendation systems
With an implicit collection, the user's actions are recorded: what the user looked at, what product was added to the cart, what he commented on, what purchase he made. The ratings are compiled automatically. The disadvantage of the method is uncertainty: if the user has looked at the product, it is not known whether he liked it or not; if the user did not buy the product, then again it is not known what caused such a decision.
Combined approach in data collection
If there is no transaction history, polls are used, but when it appears, transactions are also taken into account.
Recommender systems are a large class of models that can help almost every business. The purpose of a recommendation system is to help a business sell more by recommending a client in the right place, at the right time, and through the right communication channel in a timely manner.
However, there is a stereotype that still prevents the widespread use of recommender systems in business. It seems to many that in reality the implementation of recommendation algorithms is too difficult and requires a global restructuring of the entire process of collecting and processing data, as well as changes in business processes, logistics, and so on. Many people doubt and cannot assess what the ROI (return on investment) is in such transformations. These doubts are absolutely unfounded, because in fact, recommendation systems can be useful to almost every business, and to start recommending, often enough data that is already collected.
Relevant recommendations reduce the time it takes to find products and services, and significantly increase the likelihood of getting into the user's field of view of other objects that may be of interest to him. As a result, user loyalty and satisfaction with web services increases. Typically, users also interact with more products, and this leads to increased consumption and increased profits. In addition, newsletters, personalized advertisements, and push notifications encourage users to come back, increase the frequency of visits by repeat users, and reduce customer churn.
Today, every company simply needs to establish the process of collecting data and be able to competently and effectively use it in business, thereby optimizing and improving user content, reducing costs, increasing revenue and average check, and increasing the profitability of the business as a whole.
Developing a recommendation system based on machine learning will enable your company to:
Recommendation systems business applications include: