The effect of suspicious profiles on people recommenders

Authors:
Luiz Augusto Pizzato;Joshua Akehurst;Cameron Silvestrini;Kalina Yacef;Irena Koprinska;Judy Kay
Affiliations:
School of Information Technologies, University of Sydney, Australia;School of Information Technologies, University of Sydney, Australia;School of Information Technologies, University of Sydney, Australia;School of Information Technologies, University of Sydney, Australia;School of Information Technologies, University of Sydney, Australia;School of Information Technologies, University of Sydney, Australia
Venue:
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Year:
2012

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Abstract

As the world moves towards the social web, criminals also adapt their activities to these environments. Online dating websites, and more generally people recommenders, are a particular target for romance scams. Criminals create fake profiles to attract users who believe they are entering a relationship. Scammers can cause extreme harm to people and to the reputation of the website. This makes it important to ensure that recommender strategies do not favour fraudulent profiles over those of legitimate users. There is therefore a clear need to gain understanding of the sensitivity of recommender algorithms to scammers. We investigate this by (1) establishing a corpus of suspicious profiles and (2) assessing the effect of these profiles on the major classes of reciprocal recommender approaches: collaborative and content-based. Our findings indicate that collaborative strategies are strongly influenced by the suspicious profiles, while a pure content-based technique is not influenced by these users.