Method of Collaborative Filtration  Based on Associative Networks of Users Similarity

Meleshko, Yelyzaveta

doi:10.20998/2522-9052.2018.4.09

Cited by 5 publications

(8 citation statements)

References 4 publications

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“…Analysis of the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] showed that there are currently many methods and models for improving the quality of recommendation systems. One of the main ways to achieve this is to use hybrid RSs.…”

Section: Quality Characteristics Of Recommendation Systemsmentioning

confidence: 99%

“…In neighborhood-based models of recommendation systems [1][2][3][4][5], the creation of a user recommendation list can be divided into three separate processes:…”

Section: Quality Characteristics Of Recommendation Systemsmentioning

confidence: 99%

“…It should be noted that one of the main means of formation recommendations in hybrid systems is collaborative filtering, using either user-based or item-based approaches. In works [1][2][3][4][5][6][7] describes the basic models of collaborative filtering: memory-based and model-based.…”

Section: Quality Characteristics Of Recommendation Systemsmentioning

confidence: 99%

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The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

Meleshko

Drieiev

Al-Oraiqat³

2020

A.I.S.

Self Cite

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An n ot a t i on. The subject matter of the article is a model of calculating the user similarity coefficients of the recommendation systems. The urgency of the development is determined by the need to improve the quality of recommendation systems by adapting the time characteristics to possible changes in the similarity coefficients of users. The goal is the development of the improved model of user similarity coefficients calculation for recommendation systems to optimize the time of forming recommendation lists. The tasks to be solved are: to investigate the probability of changing user preferences of a recommendation system by comparing their similarity coefficients in time, to investigate which distribution function describes the changes of similarity coefficients of users in time. The methods used are: graph theory, probability theory, radioactivity theory, algorithm theory. Conclusions. In the course of the researches, the model of user similarity coefficients calculating for the recommendation systems has been improved. The model differs from the known ones in that it takes into account the recalculation period of similarity coefficients for the individual user and average recalculation period of similarity coefficients for all users of the system or a specific group of users. The software has been developed, in which a series of experiments was conducted to test the effectiveness of the developed method. The conducted experiments showed that the developed method in general increases the quality of the recommendation system without significant fluctuations of Precision and Recall of the system. Precision and Recall can decrease slightly or increase, depending on the characteristics of the incoming data set. The use of the proposed solutions will increase the application period of the previously calculated similarity coefficients of users for the prediction of preferences without their recalculation and, accordingly, it will shorten the time of formation and issuance of recommendation lists up to 2 times.

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Section: Quality Characteristics Of Recommendation Systemsmentioning

confidence: 99%

“…In neighborhood-based models of recommendation systems [1][2][3][4][5], the creation of a user recommendation list can be divided into three separate processes:…”

Section: Quality Characteristics Of Recommendation Systemsmentioning

confidence: 99%

See 1 more Smart Citation

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

Meleshko

Drieiev

Al-Oraiqat³

2020

A.I.S.

Self Cite

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“…If for a particular user it was not possible to identify similar users, then it is not possible to form recommendations list based on collaborative filtering for him. In such cases, additional information is used, for ex-ample, contextual information, or information about social connections, or content filtering [1,4], and it was also suggested to determine a missing similarity coefficients between users through using associative networks [7].…”

Section: The Main Materialsmentioning

confidence: 99%

Method of Generating Recommendations Lists With Considering Activity Indexes of Users in a Recommendation System

Meleshko

2019

A.I.S.

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The subject matter of the article is the process of generating recommendations lists for users of a recommendation system. The goal is to develop a new method of building recommender systems to improve the quality of recommendations lists, increase user space coverage and item space coverage with considering information about user activity indexes; development of a hybrid of this method with the method of collaborative filtering. The tasks to be solved are: to develop the method of building recommendation systems based on considering activity indexes of users, develop software to test this method, conduct experiments with the developed software to test the effectiveness of the developed method, determine the quality of its work and compare this method with the standard method of collaborative filtering. The methods used are: graph theory, mathematical statistics, the theory of algorithms, object-oriented programming. The following results were obtained: the expert-oriented method for building recommender systems based on considering indexes of user activity and calculating expert coefficients has been developed, the hybrid of this method with the collaborative filtering method has been developed, software for implementing this method and this hybrid has been developed, experiments with developed software to test the developed method and the hybrid has been conducted. Conclusions. The possibility of using information about user activity in recommender systems to improve the quality of recommendations lists has been investigated. The calculation of expert coefficients is proposed to supplement the similarity coefficients in recommender systems. An expert-oriented method for constructing recommender systems based on considering activity indexes of users and its hybrid with collaborative filtering has been developed. Experiments has been conducted with the developed software have shown that the developed method significantly improves such indicators of the recommender system as user space coverage and item space coverage and allows to create higher quality of recommendation lists without significant fluctuations Precision and Recall of the recommender system, and in some cases even improve these indicators, it depends on the features of the input data. K e ywor d s : recommendation systems; collaborative filtering; similarity coefficients; expert-oriented filtering; expert coefficients.

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“…Characteristics of the network of social interactions contain balance and transitivity; force of a user's structural position; states of dyads; the influence of the network structure on model p; probability of the existence of relations between users; dependent and independent edges of the graph; evaluation of parameters of a model; logit models [23]. Scientists gradually approached determining the parameters of information security in social networks, in particular, articles [24,25] study the parameters of trust, papers [26,27] deal with joint filtering based on the construction of associative networks of users similarity. Some researchers have moved to studying the impact of specific social network parameters directly on information security parameters where it is proved that the protection system is nonlinear [28].…”

Section: Introductionmentioning

confidence: 99%

Devising a procedure to determine the level of informational space security in social networks considering interrelations among users

Akhramovych

Shuklin

Pepa

et al. 2022

EEJET

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+ Linear and dynamic models of the system of information security in social networks, taking into consideration the relationships between users, were studied and the resistance of the security system was analyzed. There is a practical interest in studying dependence of the behavior of the system of social network security on the parameters of users’ interaction. Dynamic systems of information security in social networks in the mathematical sense of this term were considered. A dynamic system refers to any object or process, for which the concept of state as a totality of certain magnitudes at a given time is unambiguously defined and the law that describes a change (evolution) of the initial state over time was assigned. The network of social interactions consists of a totality of social users and a totality of the relations between them. Individuals, social groups, organizations, cities, countries can act as users. Relations imply not only communication interactions between users but also relations of the exchange of various resources and activities, including conflict relations. As a result of research, it was found that the security systems of a social network are nonlinear. Theoretical study of the dynamic behavior of an actual object requires the creation of its mathematical model. The procedure for developing a model is to compile mathematical equations based on physical laws. These laws are stated in the language of differential equations. Phase portraits of the data security system in the MATLAB/Multisim program, which indicate the stability of a security system in the working range of parameters even at the maximum value of the impacts, were determined. Thus, the influence of users’ interaction parameters on the parameters of the system of social network security was explored. Such study is useful and important in terms of information security in the network, since the parameters of users’ interaction significantly affect, up to 100 %, the security indicator.

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Method of Collaborative Filtration Based on Associative Networks of Users Similarity

Cited by 5 publications

References 4 publications

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

Method of Generating Recommendations Lists With Considering Activity Indexes of Users in a Recommendation System

Devising a procedure to determine the level of informational space security in social networks considering interrelations among users

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