Grant: Alliance Grants
Toronto Metropolitan University
$25,000.00
Detecting Multilingual Shillings Attacks on Recommendation Systems
EF 2022-23
Résumé
In e-commerce, successful recommendations represent tremendous potential for cross-selling and escalated average order sizes per customer, increased sales revenue, improved customer satisfaction, and controlled consumption for optimizing resources. In the 2020 era of COVID-19, by April 21, U.S. and Canadian e-commerce orders had already exhibited 129% year-over-year growth, with an impressive 146 % increase in total online retail orders. However, the wide spread of user-adaptive recommendation systems (RSs) in open platforms and information access domains such as e-commerce provides a strong motivation for unscrupulous users to inject malicious profiles, "shilling attacks" into the RSs to manipulate recommendation rankings to gain economic advantage. It is crucial to maintain the fairness of recommendations by constructing a robust system with stable recommendations in the presence of shilling attacks. Though targeted at enhancing accuracy, current detection methods fail to identify malicious users and targeted items for obfuscated attacks when attackers add noise, shift targets, shift user ratings, or use different natural language to hide their attack signature and remain undetected. In particular, there has been an increase in spamming attacks in Arabic, which aims to negatively impact the Canadian e-commerce market. The main importance and necessity of the international collaboration with the Arab Academy for Science and Technology in Egypt is to investigate and develop efficient methods of understanding the multi-lingual shilling attacks with a focus on the Arabic Language. The novel models and methods investigated in this proposed international collaboration offer efficient and robust systems to 1) protect both businesses and consumers, 2) enhance the customer shopping experience, and 3) increase sales and revenues. The main objective of the proposed work focuses on providing better marketplace opportunities that enable businesses to boost their e-commerce presence and digitize business processes so that they can perform better in the Canadian retail e-commerce market through solid training of HQP in highly desirable and cutting-edge techniques.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
578546-2022
Name nom
Kashef, Rasha R
Department département
Electrical, Computer and Biomedical Engineering
Organization id
43
Institution établissement
Toronto Metropolitan University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
25000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:1
Partie
1
Nb partie
1
Application title
Detecting Multilingual Shillings Attacks on Recommendation Systems
Application summary
In e-commerce, successful recommendations represent tremendous potential for cross-selling and escalated average order sizes per customer, increased sales revenue, improved customer satisfaction, and controlled consumption for optimizing resources. In the 2020 era of COVID-19, by April 21, U.S. and Canadian e-commerce orders had already exhibited 129% year-over-year growth, with an impressive 146 % increase in total online retail orders. However, the wide spread of user-adaptive recommendation systems (RSs) in open platforms and information access domains such as e-commerce provides a strong motivation for unscrupulous users to inject malicious profiles, "shilling attacks" into the RSs to manipulate recommendation rankings to gain economic advantage. It is crucial to maintain the fairness of recommendations by constructing a robust system with stable recommendations in the presence of shilling attacks. Though targeted at enhancing accuracy, current detection methods fail to identify malicious users and targeted items for obfuscated attacks when attackers add noise, shift targets, shift user ratings, or use different natural language to hide their attack signature and remain undetected. In particular, there has been an increase in spamming attacks in Arabic, which aims to negatively impact the Canadian e-commerce market. The main importance and necessity of the international collaboration with the Arab Academy for Science and Technology in Egypt is to investigate and develop efficient methods of understanding the multi-lingual shilling attacks with a focus on the Arabic Language. The novel models and methods investigated in this proposed international collaboration offer efficient and robust systems to 1) protect both businesses and consumers, 2) enhance the customer shopping experience, and 3) increase sales and revenues. The main objective of the proposed work focuses on providing better marketplace opportunities that enable businesses to boost their e-commerce presence and digitize business processes so that they can perform better in the Canadian retail e-commerce market through solid training of HQP in highly desirable and cutting-edge techniques.