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Many existing websites use recommendation systems for their users. They generate various offers for them, for example, similar products or recommend the people registered on this site with similar interests. Such referral mechanisms process vast amounts of information to identify potential user preferences. Recommendation systems are programs that try to determine what users want to find, what might interest them, and recommend it to them. These mechanisms have improved the interaction between the user and the site. Instead of static information, they provide dynamic information that changes: recommendations are generated separately for each user, based on his previous activity on this web resource. Information from other visitors may also be taken into account. The methods of collecting information provided by the Internet have greatly simplified the use of human thought through collaborative filtering. But, on the other hand, the large amount of information complicates the implementation of this possibility. For example, the behavior of some people is quite clearly amenable to modeling, while others behave completely unpredictably. And it is the latter that affect the shift of the results of the recommendation system and reduce its effectiveness. An analysis of Internet resources has shown that most of the recommendation systems do not provide recommendations to users, and the part that does, for example, offers products to the user, selects recommendations manually. Therefore, the task of developing methods for automated generation of recommendations for a limited set of input data is quite relevant. The problems of data sparseness, new user problem, scalability of the widely used SVD algorithm for the development of such recommendation systems are proposed to be eliminated by improving this algorithm by the method of the nearest k-neighbors. This method will allow you to easily segment and cluster system data, which will save system resources.
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