R.I.P.
π»
Ghosted
Who is Really Affected by Fraudulent Reviews? An analysis of shilling attacks on recommender systems in real-world scenarios
August 21, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Anu Shrestha, Francesca Spezzano, Maria Soledad Pera
arXiv ID
1808.07025
Category
cs.IR: Information Retrieval
Cross-listed
cs.SI
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
We present the results of an initial analysis conducted on a real-life setting to quantify the effect of shilling attacks on recommender systems. We focus on both algorithm performance as well as the types of users who are most affected by these attacks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted