Method for Determining Linguometric Coefficient Dynamics of Ukrainian Text Content Authorship

Development of the quantitative method for automated text content authorship attribution based on the statistical analysis of N-grams distribution

“…Similarly, the values of relative frequencies in Reference passage 2 and Passages 1, 2 in Fig. 3 are substantially different, so it is likely that the author of Reference passage 2 [75] is not the author of Passages 1, 2.…”

“…The obtained values of the factors, as well as analysis of graphic results, suggest that the probability of belonging of Passage 1 [76] to Reference passage 1 [77] is higher than for Passage 2 [75].…”

“…The availability of electronic devices makes it possible to push back the authorship attribution with the involvement of a large number of experts, to accelerate and simplify this process through its automation [74]. The notion of the authorship attribution is defined as the process of finding the author by many general and private features of a text that constitute the author's style [75].…”

Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

“…It uses the elements' functions to compare an element to users' profiles and to provide recommendations ( Fig. 2) [72]. The quality of recommendations is limited by the selected functions of the recommended elements.…”

“…KBF applies knowledge about users and goods to substantiate which goods meet the requirements of users, and generate appropriate recommendations [4]. A special type of KBF is the RS based on the constraints [72]. They can recommend technically-sophisticated products that are rarely purchased (for example, cars, houses) and detect important limitations for the user (for example, price) [5].…”