Multi-level hybrid recommender decision support system with verbal output

Abstract

The article describes decision-making methods based on intelligent learning algorithms, for the construction of which verbal elements are used. Such algorithms and methods operate in calculations with strictly quantitative data, however, taking into account the human way of perceiving information in verbal form. A person does not directly participate in the process of constructing a model, that is, its structure does not depend on expert or other human opinions, however, qualitative verbal information (for example, elements of regulatory acts, documents, orders, etc.) is embedded in the algorithm in encoded form. Unlike the classical methods of fuzzy logic and fuzzy inference, the proposed models consist of several interconnected classical and author’s models, using author’s numerical methods. This increases their accuracy and adequacy. The great advantage is the automatic way to build these models, only on the basis of sets of initial quantitative and qualitative data, which will allow you to quickly and efficiently create recommender systems for solving a wide range of decision-making problems. The system is built in three stages: First, the fuzzy logical inference Takagi – Sugeno – Kang system (TSK) is automatically built on the sounding of the available quantitative and qualitative data. The advantage of such a system is that it can be retrained (adjust its parameters). Then, after the system is built according to the available data, at the second stage its accuracy is increased using a fuzzy neural network. The drawback of the constructed system is that it does not give out a verbal answer. At the third stage, based on the adjusted parameters of the TSK system, a Mamdani type system is constructed using a special algorithm. As a result, it can produce a verbal answer from a recommender system, and at the same time gets all the advantages of a neural network refinement. Computational experiments are presented.