Method for Detecting Shilling Attacks Based on Implicit Feedback in Recommender Systems

Chala, Oksana; Novikova, Lyudmyla; Chernyshova, Larysa; Kalnitskaya, Angelika

doi:10.21303/2461-4262.2020.001394

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Для побудови рекомендацій такі системи використовують інформацію про продажі та рейтинги товарів, а також про зміну останніх з часом [1,3]. Однак рейтинги можуть бути спотворені в результатів шилінг-атак [4,5]. Рекомендований перелік товарів або послуг після атаки відображає вподобання не користувача, а атакуючого.…”

Section: вступunclassified

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Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

Чалий¹,

Лещинський²

2020

Scientific journal “Bionics of Intelligence”

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The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

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“…Отримані патерни відповідають моделям (4) та (5). Патерн альтернативного вибору містить два базових варіанти поведінки користувача.…”

Section: рис 3 патерни послідовно-альтернативного вибору користувачівunclassified

Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

Чалий¹,

Лещинський²

2020

Scientific journal “Bionics of Intelligence”

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“…Recommendations for target users are formed based on information about the choice of goods and services by similar users, as well as information about product ratings. Ratings reflect the degree of popularity of users, but they can be falsified as a result of attacks [4,5] by virtual users, or shilling attacks.…”

Section: Introductionmentioning

confidence: 99%

Construction of Patterns of User Preferences Dynamics for Explanations in the Recommender System

Chalyi

Leshchynskyi

2021

A.I.S.

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The subject of study in the article is the processes of constructing explanations in recommendation systems. Objectives. The goal is to develop a method of constructing patterns that reflect the dynamics of user preferences and provide an opportunity to form an explanation of the recommended list of items, taking into account changes in the user’s requirements of the recommendation system. Construction of explanations taking into account the dynamics of changes in consumer preferences makes it possible to increase user confidence in the results of the intelligent system. Tasks: structuring models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system; development of a method for constructing patterns of changing user preferences using process mining technology; experimental verification of the method for constructing patterns of changing consumer preferences. The approaches used are: temporal logics, which determine the approaches to the description of the temporal ordering of a set of events. The following results are obtained. The structuring of models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system is performed; developed and performed an experimental test of the method of constructing patterns of user preferences dynamics. Conclusions. The scientific novelty of the results is as follows. The method of dynamics patterns construction of users’ preferences for the formation of explanations concerning the recommended list of subjects is offered. The method sequentially generates a set of ordered events, each of which reflects the choice of the subject by a group of users at a certain time interval, and also builds a graph representation of the patterns of user preferences through intellectual analysis of processes. The patterns obtained as a result of the method consist of time-ordered pairs of events that reflect the knowledge of changing user preferences over time. Further use of such dependencies as elements of the knowledge base makes it possible based on probabilistic inference to build a set of alternative explanations for the received recommendation, and then arrange these explanations according to the probability of their implementation for the recommended list of subjects.

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Method for Detecting Shilling Attacks Based on Implicit Feedback in Recommender Systems

Cited by 2 publications

References 15 publications

Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

Construction of Patterns of User Preferences Dynamics for Explanations in the Recommender System

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