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Credrec: A Unified Framework for Handling Shilling Attacks and Generating Credible Recommendations

https://doi.org/10.2139/ssrn.4463658
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1 of the 32 references listed on this page is in the Retraction Watch database as retracted (export 2026-10-28): 10.1007/s00500-021-05586-8 (notice 2024-10-28).

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-00916-8_43openalex-extracted
Collaborative Shilling Detection Bridging Factorization and User Embedding
inferredcites →https://doi.org/10.1007/978-3-030-16145-3_29openalex-extracted
Jaccard Coefficient-Based Bi-clustering and Fusion Recommender System for Solving Data Sparsity
inferredcites →https://doi.org/10.1007/s00500-021-05586-8openalex-extracted
RETRACTED ARTICLE: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques
inferredcites →https://doi.org/10.1007/s00521-018-3959-2openalex-extracted
A collaborative filtering recommendation algorithm based on information theory and bi-clustering
inferredcites →https://doi.org/10.1007/s10462-018-9655-xopenalex-extracted
Shilling attacks against collaborative recommender systems: a review
inferredcites →https://doi.org/10.1007/s10844-017-0489-9openalex-extracted
A survey of book recommender systems
inferredcites →https://doi.org/10.1007/s11771-014-2334-4openalex-extracted
Applying memetic algorithm-based clustering to recommender system with high sparsity problem
inferredcites →https://doi.org/10.1007/s12652-019-01321-2openalex-extracted
Shilling attack detection in binary data: a classification approach
inferredcites →https://doi.org/10.1016/j.advengsoft.2013.12.007openalex-extracted
Grey Wolf Optimizer
inferredcites →https://doi.org/10.1016/j.is.2022.102154openalex-extracted
A detection method for hybrid attacks in recommender systems
inferredcites →https://doi.org/10.1016/j.jisa.2020.102493openalex-extracted
Recommendation attack detection based on deep learning
inferredcites →https://doi.org/10.1016/j.knosys.2016.02.008openalex-extracted
Re-scale AdaBoost for attack detection in collaborative filtering recommender systems
inferredcites →https://doi.org/10.1016/j.knosys.2018.02.032openalex-extracted
UD-HMM: An unsupervised method for shilling attack detection based on hidden Markov model and hierarchical clustering
inferredcites →https://doi.org/10.1016/j.knosys.2019.04.001openalex-extracted
Detecting shilling attacks in recommender systems based on analysis of user rating behavior
inferredcites →https://doi.org/10.1016/j.neucom.2015.12.137openalex-extracted
SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems
inferredcites →https://doi.org/10.1093/comjnl/bxy008openalex-extracted
A shilling attack detector based on convolutional neural network for collaborative recommender system in social aware network
inferredcites →https://doi.org/10.1108/oir-03-2015-0073openalex-extracted
Discovering shilling groups in a real e-commerce platform
inferredcites →https://doi.org/10.1109/icaica52286.2021.9497958openalex-extracted
Group attack detection in recommender systems based on triangle dense subgraph mining
inferredcites →https://doi.org/10.1109/icaica52286.2021.9498095openalex-extracted
Detecting group shilling attacks in recommender systems based on maximum dense subtensor mining
inferredcites →https://doi.org/10.1109/icmlc.2018.8526965openalex-extracted
Detection Of Shilling Attack In Collaborative Filtering Recommender System By Pca And Data Complexity
inferredcites →https://doi.org/10.1109/icwapr.2016.7731644openalex-extracted
Shilling attack detection in collaborative filtering recommender system by PCA detection and perturbation
inferredcites →https://doi.org/10.1109/tcss.2020.3013878openalex-extracted
Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering
inferredcites →https://doi.org/10.1109/tii.2014.2308433openalex-extracted
An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems
inferredcites →https://doi.org/10.1109/tkde.2019.2946247openalex-extracted
BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender Systems
inferredcites →https://doi.org/10.1109/vcon.2010.28openalex-extracted
Memetic Collaborative Filtering Based Recommender System
inferredcites →https://doi.org/10.1145/1721654.1721677openalex-extracted
Collaborative filtering with temporal dynamics
inferredcites →https://doi.org/10.1145/2505515.2505700openalex-extracted
Uncovering collusive spammers in Chinese review websites
inferredcites →https://doi.org/10.1145/2600428.2609483openalex-extracted
Detection of abnormal profiles on group attacks in recommender systems
inferredcites →https://doi.org/10.1155/2021/2907691openalex-extracted
An Unsupervised Approach for Detecting Group Shilling Attacks in Recommender Systems Based on Topological Potential and Group Behaviour Features
inferredcites →https://doi.org/10.4018/ijkss.2019040102openalex-extracted
A Study of Recent Recommender System Techniques
inferredcites →https://doi.org/10.4156/jcit.vol5.issue8.13openalex-extracted
Genetic Algorithms for Feature Weighting in Multi-criteria Recommender Systems
inferredcites →https://doi.org/10.48550/arxiv.1506.04584openalex-extracted
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