Method For Detecting Shilling Attacks In E-commerce Systems Using Weighted Temporal Rules

The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities

Chiron: A Robust Recommendation System with Graph Regularizer

Recommendation systems have been widely used by commercial service providers for giving suggestions to users. Collaborative filtering (CF) systems, one of the most popular recommendation systems, utilize the history of behaviors of the aggregate user-base to provide individual recommendations and are effective when almost all users faithfully express their opinions. However, they are vulnerable to malicious users biasing their inputs in order to change the overall ratings of a specific group of items. CF systems largely fall into two categories - neighborhood-based and (matrix) factorization-based - and the presence of adversarial input can influence recommendations in both categories, leading to instabilities in estimation and prediction. Although the robustness of different collaborative filtering algorithms has been extensively studied, designing an efficient system that is immune to manipulation remains a significant challenge. In this work we propose a novel "hybrid" recommendation system with an adaptive graph-based user/item similarity-regularization - "Chiron". Chiron ties the performance benefits of dimensionality reduction (through factorization) with the advantage of neighborhood clustering (through regularization). We demonstrate, using extensive comparative experiments, that Chiron is resistant to manipulation by large and lethal attacks

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development

Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering

This paper examines the effect of Recommender Systems in security oriented issues. Currently research has begun to evaluate the vulnerabilities and robustness of various collaborative recommender techniques in the face of profile injection and shilling attacks. Standard collaborative filtering algorithms are vulnerable to attacks. The robustness of recommender system and the impact of attacks are well suited this study and examined in this paper. The predictability issues and the various attack strategies are also discussed. Based on KNN the robustness of the recommender system were examined and the sensitivity of the rating given by the users are also analyzed. Furthermore the robust PLSA also considered for the work

METHOD FOR DETECTING SHILLING ATTACKS BASED ON IMPLICIT FEEDBACK IN RECOMMENDER SYSTEMS

The problem of identifying shilling attacks, which are aimed at forming false ratings of objects in the recommender system, is considered. The purpose of such attacks is to include in the recommended list of items the goods specified by the attacking user. The recommendations obtained as a result of the attack will not correspond to customers' real preferences, which can lead to distrust of the recommender system and a drop in sales. The existing methods for detecting shilling attacks use explicit feedback from the user and are focused primarily on building patterns that describe the key characteristics of the attack. However, such patterns only partially take into account the dynamics of user interests. A method for detecting shilling attacks using implicit feedback is proposed by comparing the temporal description of user selection processes and ratings. Models of such processes are formed using a set of weighted temporal rules that define the relationship in time between the moments when users select a given object. The method uses time-ordered input data. The method includes the stages of forming sets of weighted temporal rules for describing sales processes and creating ratings, calculating a set of ratings for these processes, and forming attack indicators based on a comparison of the ratings obtained. The resulting signs make it possible to distinguish between nuke and push attacks. The method is designed to identify discrepancies in the dynamics of purchases and ratings, even in the absence of rating values at certain time intervals. The technique makes it possible to identify an approach to masking an attack based on a comparison of the rating values and the received attack indicators. When applied iteratively, the method allows to refine the list of profiles of potential attackers. The technique can be used in conjunction with pattern-oriented approaches to identifying shilling attack

A Survey on True-reputation Algorithm for Trustworthy Online Rating System

The average of customer ratings on a product, which we call a reputation, is one of the key factors in online shoping. The common way for customers to express their satisfaction level with their purchases is through online ratings. The overall buyer?s satisfaction is quantified as the aggregated score of all ratings and is available to all buyers. This average score and reputation of a product acts as a guide for online buyers and highly influences consumer?s final purchase decisions. The trustworthiness of a reputation can be achieved when a large number of buyers involved in ratings with honesty. If some users wantedly give unfair ratings to a item, especially when few users have participated, the reputation of the product could easily be modified. In order to improve the trustworthiness of the products in e-commerce sites a new model is proposed with a true - reputation algorithm that repeatedly adjusts the reputation based on the confidence of the user ratings

METHOD FOR DETECTING SHILLING ATTACKS IN E-COMMERCE SYSTEMS USING WEIGHTED TEMPORAL RULES

The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities