Strategic Attacks on Recommender Systems: An Obfuscation Scenario-Reference-Cited by-同舟云学术

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Strategic Attacks on Recommender Systems: An Obfuscation Scenario

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA)
language:
Short-container-title:

Author:

Al Jurdi Wissam¹^ORCID,Abdo Jacques Bou²^ORCID,Demerjian Jacques³^ORCID,Makhoul Abdallah⁴^ORCID

Affiliation:

1. FEMTO-ST Institute, CNRS - LaRRIS, University Bourgogne Franche-Comté - Lebanese University,Faculty of Sciences,Montbéliard,France

2. University of Nebraska at Kearney,Kearney,USA

3. LaRRIS, Lebanese University,Faculty of Sciences,Fanar,Lebanon

4. FEMTO-ST Institute, CNRS, University Bourgogne Franche-Comté,Montbéliard,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10017434/10017304/10017953.pdf?arnumber=10017953

Reference28 articles.

1. I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems

2. Rate it again

3. Shilling attacks against collaborative recommender systems: a review

4. A Practical Privacy-Preserving Recommender System

5. Detecting shilling attacks in recommender systems based on analysis of user rating behavior;cai;PLoS ONE,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring the Power of Weak Ties on Serendipity in Recommender Systems;Studies in Computational Intelligence;2024

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