Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

Alonso, Santiago; Bobadilla, Jesús; Ortega, Fernando; Moya, Ricardo

doi:10.1109/access.2019.2905862

Cited by 36 publications

(20 citation statements)

References 47 publications

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“…Alonso et al [67] calculated a reliability value for each prediction of a user to an item. When an unusual change is observed in the item prediction's reliability value, it indicates a possible shilling attack.…”

Section: Defense Against Shilling Attacksmentioning

confidence: 99%

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

et al. 2020

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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.

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Section: Defense Against Shilling Attacksmentioning

confidence: 99%

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

et al. 2020

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“…After applying expressions (12) and (13) in (8) and (9) we get the F-SGD weight update rules for user features and items features vectors as:…”

Section: A Fractional Sgdmentioning

confidence: 99%

“…There are different types of recommender systems based on different methods [8]- [13] such as collaborative filtering (CF), content based filtering (CB), demographic, knowledgebased, community-based and hybrid recommender systems. Widely applied techniques among those are CF [14]- [18] and CB [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems

Khan

Zubair²,

Alquhayz

et al. 2019

IEEE Access

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The demand for recommender systems in E-commerce industry has increased tremendously. Efficient recommender systems are being proposed by different E-business companies with the intention to give users accurate and most relevant recommendation of products from huge amount of information. To improve the performance of recommender systems, various stochastic variants of gradient descent based algorithms have been reported. The scalability requirement of recommender systems needs algorithms with fast convergence to generate recommendations of specific items. Using the concepts of fractional calculus, an efficient variant of the stochastic gradient descent (SGD) was developed for fast convergence. Such fractional SGD (F-SGD) is further accelerated by adding a momentum term, thus termed as momentum fractional stochastic gradient descent (mF-SGD). The proposed mF-SGD method is shown to offer improved estimation accuracy and convergence rate, as compared to F-SGD and standard momentum SGD for different proportions of previous gradients, fractional orders, learning rates and number of features.

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“…This system has become a commercial platform that assists the user in providing suggestions for items to be selected. The provided suggestions are useful to support users in various decision-making processes, such as what books to read, which locations to visit, what news to read, and more [7]. Based on the utilized data source and computation method, the recommendation system is divided into three approaches, namely: collaborative filtering, contentbased filtering, and hybrid filtering [6,8].…”

Section: Introductionmentioning

confidence: 99%

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

Widiyaningtyas

Hidayah

Adji

2020

Preprint

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A recommendation system is a software used in the e-commerce field that provides recommendations for customers to choose the items they like. Several recommendation systems have been proposed; however, collaborative filtering is the most widely used approach. The main issue in collaborative filtering is how to implement a similarity algorithm that can improve performance in the recommendation system. Several similarity algorithms based on user rating value have been developed, and recently a similarity algorithm has been developed that combines the user rating value and the user behavior value. However, the existing research is still based only on a single user behavior value, which is the genre data. Therefore, we propose a new similarity algorithm that considers not only the genre data but also the user profile data (namely age, gender, occupation, and location). The new similarity we are proposing is called User Profile Correlation-based Similarity (UPCSim). The user profile correlation similarity was obtained by calculating the correlation coefficient between the user profile data and the user rating or behavior values. An experiment was done to compare the accuracy of the UPCSim algorithm with that of the previous algorithm. The experiment results show that the UPCSim algorithm can improve the recommendation performance MAE by 1.64% and RMSE by 1.4% compared to the previous algorithm.

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Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

Cited by 36 publications

References 47 publications

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey

Design of Momentum Fractional Stochastic Gradient Descent for Recommender Systems

User Profile Correlation-Based Similarity Algorithm in Movie Recommendation System

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