Work

Recommender Systems and their Security Concerns

Year: 2015

Type: conference-paper

Abstract: Instead of simply using two-dimensional User × Item features, advanced recommender systems rely on more additional dimensions (e.g. time, location, social network) in order to provide better recommendation services. In the first part of this paper, we will survey a variety of dimension features and show how they are integrated into the recommendation process. When the service providers collect more and more personal information, it brings great privacy concerns to the public. On another side, the service providers could also suffer from attacks launched by malicious users who want to bias the recommendations. In the second part of this paper, we will survey attacks from and against recommender service providers, and existing solutions.

Source: Open Repository and Bibliography (University of Luxembourg)

Authors: Jun Wang, Qiang Tang

Institutions: University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Université du Luxembourg, Fonds National de la Recherche Luxembourg

OpenAlex ID: W2397351226

FWCI: 0.236

Cites: 191

Cited by: 6

Related to: 10