Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

Lecture notes in computer sciencePublished 1 January 2006

Bamshad Mobasher, Robin Burke, Chad Williams, Runa Bhaumik

DOI10.1007/11891321_6

Source URL

Citations67

SJR quartileQ2

SJR score0.35

SNIP0.55

TL;DR

This chapter explores an attack model that focuses on a subset of users with similar tastes and shows that such an attack can be highly successful against both user-based and item-based collaborative filtering.

Abstract

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.

Keywords

Computer ScienceDecision Sciences

Item-based collaborative filtering recommendation algorithms

9,017 Citations2001Badrul Sarwar, George Karypis +2 more

This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

ACM Transactions on Information SystemsEvaluating collaborative filtering recommender systems

5,762 Citations2004Jonathan L. Herlocker, Joseph A. Konstan +2 more

The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.

GroupLens

4,975 Citations1994Paul Resnick, Neophytos Iacovou +3 more

View PDF

ACM SIGIR ForumAn Algorithmic Framework for Performing Collaborative Filtering

2,552 Citations2017Jonathan L. Herlocker, Joseph A. Konstan +2 more

An algorithmic framework for performing collaborative filtering

1,973 Citations1999Jonathan L. Herlocker, Joseph A. Konstan +2 more

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage.

View PDF

Shilling recommender systems for fun and profit

643 Citations2004Shyong K. Lam, John Riedl

Four open questions are explored that may affect the effectiveness of shilling attacks on recommender systems: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked.

ACM Transactions on Internet TechnologyCollaborative recommendation

327 Citations2004Michael P. O’Mahony, Neil Hurley +2 more

This work analyzes the robustness of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings, and formalizes recommendation accuracy in machine learning terms and develops theoretically justified models of accuracy.

Preventing shilling attacks in online recommender systems

310 Citations2005Paul‐Alexandru Chirita, Wolfgang Nejdl +1 more

Several metrics for analyzing rating patterns of malicious users are proposed and an algorithm for protecting recommender systems against shilling attacks is evaluated that can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.

Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems

94 Citations2005Bamshad Mobasher, Robin Burke +2 more

This paper proposes a new attack model that focuses on a subset of users with similar tastes and shows that such an attack can be highly successful against an item-based algorithm.

Identifying Attack Models for Secure Recommendation

83 Citations2004Robin Burke, Bamshad Mobasher +2 more

The setting, organisation and evaluation of the first written examination held during the EuroEcho 7 meeting in Barcelona on December 6, 2003 is described, which drew on established accreditation processes including those of the American National Board of Echocardiography, Cardiovascular Credentialling International and the British Society of E chocardiographers.