Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules

L-Band MMICs for Space-based SAR system

The design and performance of an L-Band GaAs chip-set is presented.The chip-set consists of a 6-bit attenuator circuit,a Low-Noise Amplifier (LNA)and a Multi Function Chip that is the combination of a 6-bit attenuator and 6-bit Phase shifter circuit.The chip-set is developed for the pre-flight engineering T/R (Transmit and Receive)modules currently in development with Astrium in a space-based SAR (Synthetic Aperture Radar)system.The MMICs are realised in the 0.25 µm PHEMT (PH25)technology of UMS.Only one iteration was needed for the MMICs in order to be fully compliant with the specifications

Ranking and selecting association rules based on dominance relationship

The huge number of association rules represent the main obstacle that a decision maker faces. In order to bypass this obstacle, an efficient selection of rules must be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures caused a new problem which is the heterogeneity of the evaluation results and this created confusion to the decision. In this scope, we propose a novel approach to discover interesting association rules without favouring or excluding any measure by adopting the notion of dominance between rules. Our approach bypasses the problem of measure heterogeneity and find a compromise between their evaluations and also bypasses another non-trivial problem which is the threshold value specification

When Engagement Meets Similarity: Efficient (k, r)-Core Computation on Social Networks.

In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we also consider the pairwise similarity between users based on their profiles. For a given similarity metric and a similarity threshold r, the similarity between any two vertices in a (k,r)-core is ensured not less than r. Efficient algorithms are proposed to enumerate all maximal (k,r)-cores and find the maximum (k,r)-core, where both problems are shown to be NP-hard. Effective pruning techniques significantly reduce the search space of two algorithms and a novel (k,k')-core based (k,r)-core size upper bound enhances performance of the maximum (k,r)-core computation. We also devise effective search orders to accommodate the different nature of two mining algorithms. Comprehensive experiments on real-life data demonstrate that the maximal/maximum (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of two mining algorithms is significantly improved by proposed techniques

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

Estudio de técnicas de ataques en sistemas de recomendación aplicados al dominio turístico

Debido a la aparición de la Web2.0, los sistemas de recomendación han tenido un gran desarrollo en las ultimas décadas. Ante la era de la información masiva, los métodos de recomendación se presentan como una manera eficiente de filtrar información y escoger lo que realmente se quiere. El método más común y mejor valorado en la industria y la comunidad científica es el filtrado colaborativo. Este tipo de técnica se basa en la similitud de los ítems o del perfil de usuario, por lo que resulta bastante vulnerable a los ataques externos. En este contexto, el propósito de este trabajo es estudiar los diferentes ataques conocidos hasta ahora y poder detectarlos mediante algoritmos de aprendizaje automático. En este documento se ha estudiado en profundidad la detección de los ataques basado en filtrado colaborativo así como los principales trabajos de investigación, lo cual ha producido las siguientes contribuciones: 1. En base al funcionamiento del filtrado colaborativo, se ha investigado sobre los conceptos de ataques y detecciones en este tipo de mecanismo. 2. Basándose en la estrategia de ataque se puede distinguir dos tipos: los estándares y los de confusión. En este trabajo se van a seleccionar los 3 tipos de ataques estándares más comunes: RandomAttack, AverageAttack, BandwagonAttack. Además, también se va a implementar un tipo de ataque híbrido que resulte de la combinación cualesquiera de los tres. Tras ejecutar las inyecciones de perfiles en sistema, se va a intentar evaluar la efectividad de dicho ataque mediante las métricas de HitRatio y Prediction shift. En esta parte se ha visto que fillerSize es un factor decisivo durante el proceso de ataque, ya que en numerosos escenarios este parámetro define el nivel de similitud entre ítems. En cambio, en aquellos sistemas que tienen una matriz de similitud densa, el factor attackSize es el que domina ya que hay una gran posibilidad de puntar un ítem popular. 3. Entender los algoritmos de detección basados en aprendizaje automático: BayesDetector, SemiSAD, PCASelectUsers. Analizar la idea básica de cada algoritmo y su proceso de implementación. Una vez implementados los modelos de detección se va a intentar realizar una evaluación sobre los distintos tipos de ataques mediante Precision, Recall y F-measure. Tras analizar los resultados obtenidos mediante estas métricas, llegamos a la conclusión de que la técnica de SemiSAD ha sido el mejor método

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods

ДОСЛІДЖЕННЯ РОБАСТНОСТІ РЕКОМЕНДАЦІЙНИХ СИСТЕМ З КОЛАБОРАТИВНОЮ ФІЛЬТРАЦІЄЮ ДО ІНФОРМАЦІЙНИХ АТАК

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 memory-based 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.У даній статті здійснено дослідження робастності рекомендаційних систем з колаборативною фільтрацією до інформаційних атак, метою яких є накручування рейтингів деяких об’єктів системи. Досліджено вразливості методів колаборативної фільтрації до інформаційних атак, а також розглянуто основний вид атак на рекомендаційні системи – атаку ін’єкцією профілів. Розглянуто способи оцінки робастності рекомендаційних систем до атак ін’єкцією профілів за допомогою таких показників як середній зсув прогнозування оцінок та коефіцієнт звернень користувачів до рекомендацій. Описано загальний спосіб тестування робастності рекомендаційних систем. Наведено класифікацію методів колаборативної фільтрації та здійснено порівняння їх робастності до інформаційних атак. Виявлено, що методи колаборативної фільтрації засновані на моделі більш робастні, ніж методи засновані на пам’яті, а методи на основі коефіцієнтів подоби об’єктів, більш стійкі до атак, ніж методи засновані на коефіцієнтах подоби користувачів. Досліджено методи виявлення інформаційних атак на рекомендаційні системи на основі класифікації профілів користувачів. Розглянуто показники, на основі яких можна виявити як окремі профілі ботів у системі, так і групи ботів. Наведено способи оцінки якості роботи класифікаторів профілів користувачів, зокрема, обчислення таких показників як точність, повнота, точність негативного прогнозу та специфічність. Розглянуто спосіб підвищення робастності рекомендаційних систем за допомогою введення параметра репутація користувачів, а також методів одержання числового значення параметру репутації користувачів. Результати даних досліджень у подальшому будуть спрямовані на розробку програмної моделі рекомендаційної системи для тестування робастності різних алгоритмів колаборативної фільтрації до відомих інформаційних ата