Trust based recommendation systems

Özsoy, Makbule Gülçin; Polat, Faruk

doi:10.1145/2492517.2500276

Cited by 23 publications

(9 citation statements)

References 31 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%

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

Meleshko

Drieiev

Al-Oraiqat³

2020

A.I.S.

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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%

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

Meleshko

Drieiev

Al-Oraiqat³

2020

A.I.S.

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“…Experimental analysis has been performed on three real world datasets to show that the method proposed outperforms several other systems. Where as in [7] it states a review of recommendation systems, trust reputation systems and their combinations. Use of trust has emerged as a new way to find better recommendation.…”

Section: Synrec: a Prediction Technique Using Collaborative Filteringmentioning

confidence: 99%

SynRec: A Prediction Technique using Collaborative Filtering and Synergy Score

Kalra¹,

Yadav²,

Bathla³

2019

IJEAT

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Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

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“…Для боротьби з інформаційними атаками на рекомендаціні системи може бути корисним додавання параметру репутація для користувачів системи [1,[12][13][14].…”

Section: використання репутації користувачів для підвищення робастносunclassified

The Research to the Robustness of Recommendation Systems With Collaborative Filtering to Information Attacks

2019

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In this article research to the robustness of recommendation systems with collaborative filtering to information attacks, which are aimed at raising or lowering the ratings of target objects in a system. The vulnerabilities of collaborative filtering methods to information attacks, as well as the main types of attacks on recommendation systems - profile-injection attacks are explored. Ways to evaluate the robustness of recommendation systems to profile-injection attacks using metrics such as rating deviation from mean agreement and hit ratio are researched. The general method of testing the robustness of recommendation systems is described. The classification of collaborative filtration methods and comparisons of their robustness to information attacks are presented. Collaborative filtering model-based methods have been found to be more robust than memorybased methods, and item-based methods more resistant to attack than user-based methods. Methods of identifying information attacks on recommendation systems based on the classification of user-profiles are explored. Metrics for identify both individual bot profiles in a system and a group of bots are researched. Ways to evaluate the quality of user profile classifiers, including calculating metrics such as precision, recall, negative predictive value, and specificity are described. The method of increasing the robustness of recommendation systems by entering the user reputation parameter as well as methods for obtaining the numerical value of the user reputation parameter is considered. The results of these researches will in the future be directed to the development of a program model of a recommendation system for testing the robustness of various algorithms for collaborative filtering to known information attacks.

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Trust based recommendation systems

Cited by 23 publications

References 31 publications

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

The Improved Model of User Similarity Coefficients Computation for Recommendation Systems

SynRec: A Prediction Technique using Collaborative Filtering and Synergy Score

The Research to the Robustness of Recommendation Systems With Collaborative Filtering to Information Attacks

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