Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system

Cao, Jie; Wu, Zhang; Mao, Bo; Zhang, Yanchun

doi:10.1007/s11280-012-0164-6

Cited by 114 publications

(47 citation statements)

References 19 publications

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“…There are existing works try to bridge collaborative filtering and SSL [8,49,50]. For example, Semi-SAD [8] tried to combine the idea of collaborative filtering and SSL for shilling attack detection tasks. The semi-supervised co-training (CSEL) framework [50] first proposes two context-aware factorization models by leveraging âĂĲmore generalâĂİ sources such as age and gender of a user, or the genres.…”

Section: Chain Graph Modelsmentioning

confidence: 99%

Semi-supervised Learning Meets Factorization

Chen

Chang

et al. 2018

ACM Trans. Knowl. Discov. Data

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Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this paper, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.Latent factor model. Among the existing recommendation approaches, latent factor model (LFM) has been drawing much attention due to its good performance and scalability. LFM uses a low dimensional user and item latent factors to represent the characteristics of each user and each item, and uses the product of them to represent the user's rating on the item. LFM has drawn much attention recently due to its good performance and scalability [1,4,19,24,26,28,34,35,37,43,44,47]. As users have actions (e.g., rate and buy) on some items, LFM aims to predict the users' unknown actions on other items. The tendency of a user's action on an item can be indicated by a real-valued number, i.e., rating or label. Thus, the recommendation problem is also known as the unknown ratings prediction problem [41]. In practice, however, many LFMs have to evaluate very large user and item sets, where the user-item (U-I) matrix is extremely sparse-such data sparsity has always been its main challenge [42]. Semi-supervised learning. SSL uses unlabeled data to either modify or reprioritize hypotheses obtained from labeled data alone, and thus can alleviate the label sparsity problem by adopting the graph information between data [40]. Towards effective SSL, affinity graph-based smoothness approaches have attracted much research interests, which follow the smoothness insight: close nodes on an affinity graph have similar labels. Graph-based SSL is appealing recently because it is easy to implement and gives rise to closed-form solutions [9,13,16,45,46,52]. However, graph-based SSL directly predicts the unknown ratings in the original U-I matrix, and thus suffers from the scalability problem.As a key insight of this paper, we identify the marriage of SSL and LFM. The main insights of SSL (i.e., smoothness...

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Section: Chain Graph Modelsmentioning

confidence: 99%

Semi-supervised Learning Meets Factorization

Chen

Chang

et al. 2018

ACM Trans. Knowl. Discov. Data

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“…Bilge et al (2014b) use the idea of bisecting k-means clustering as a shilling attack detection method. Some researchers apply semi-supervised learning approaches in attack detection (Wu et al, 2011;Cao et al, 2013). Another group of research focus on analyzing the robustness of various CF algorithms with respect to shilling attacks, and proposing robust algorithms.…”

Section: Related Workmentioning

confidence: 99%

Robustness analysis of arbitrarily distributed data-based recommendation methods

Yılmazel

Kaleli

2016

Expert Systems with Applications

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“…Shilling attack detection and robust attack-resistant CF have attracted significant attention in recent years. For detection, supervised learning-based detection systems using several user features [9][10][11], unsupervised detection based on clustering algorithms [12,13], and semi-supervised detection using both labeled and unlabeled data [14,15] are available. For robust attack-resistant CF, trust-aware CF based on constructing a user trust model [16,17] and item anomaly detection-based robust CF [18] are available.…”

Section: Introductionmentioning

confidence: 99%

From similarity perspective: a robust collaborative filtering approach for service recommendations

Gao

Lü

Yang

et al. 2019

Front. Comput. Sci.

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Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions. We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.

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Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system

Cited by 114 publications

References 19 publications

Semi-supervised Learning Meets Factorization

Semi-supervised Learning Meets Factorization

Robustness analysis of arbitrarily distributed data-based recommendation methods

From similarity perspective: a robust collaborative filtering approach for service recommendations

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