STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Using Singular Value Decomposition Approximation for Collaborative Filtering
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Robust 3D Reconstruction with Outliers Using RANSAC Based Singular Value Decomposition
IEICE - Transactions on Information and Systems
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Unsupervised shilling detection for collaborative filtering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Attack resistant collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
PITTCULT: trust-based cultural event recommender
Proceedings of the 2008 ACM conference on Recommender systems
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Strategies for Effective Shilling Attacks against Recommender Systems
Privacy, Security, and Trust in KDD
Time-Dependent Models in Collaborative Filtering Based Recommender System
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Effective diverse and obfuscated attacks on model-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Rating aggregation in collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The role of user mood in movie recommendations
Expert Systems with Applications: An International Journal
Analysis of robustness in trust-based recommender systems
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Improving recommendations using WatchingNetworks in a social tagging system
Proceedings of the 2011 iConference
Mining social media to create personalized recommendations for tourist visits
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Trust-based local and social recommendation
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
DIGTOBI: a recommendation system for Digg articles using probabilistic modeling
Proceedings of the 22nd international conference on World Wide Web
A unified framework for reputation estimation in online rating systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit. Robust statistics is an area within statistics where estimation methods have been developed that deteriorate more gracefully in the presence of unmodeled noise and slight departures from modeling assumptions. In this work, we study how such robust statistical methods, in particular M-estimators, can be used to generate stable recommendation even in the presence of noise and spam. To that extent, we present a Robust Matrix Factorization algorithm and study its stability. We conclude that M-estimators do not add significant stability to recommendation; however the presented algorithm can outperform existing recommendation algorithms in its recommendation quality.