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.
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References
Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T.: Ccr - a content-collaborative reciprocal recommender for online dating. In: Proceedings of the 22nd IJCAI, Barcelona, Spain (July 2011)
Brožovský, L., Petříček, V.: Recommender system for online dating service. CoRR abs/cs/0703042 (2007)
Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., Mahidadia, A.: Collaborative Filtering for People to People Recommendation in Social Networks. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 476–485. Springer, Heidelberg (2010)
Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: CHI 2009, pp. 201–210. ACM, New York (2009)
Diaz, F., Metzler, D., Amer-Yahia, S.: Relevance and ranking in online dating systems. In: Proceeding of the 33rd SIGIR, pp. 66–73. ACM, New York (2010)
Hernández, M.A., Stolfo, S.J.: The merge/purge problem for large databases. SIGMOD Rec. 24, 127–138 (1995)
Kim, Y.S., Mahidadia, A., Compton, P., Cai, X., Bain, M., Krzywicki, A., Wobcke, W.: People Recommendation Based on Aggregated Bidirectional Intentions in Social Network Site. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS, vol. 6232, pp. 247–260. Springer, Heidelberg (2010)
Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th WWW, pp. 393–402. ACM, New York (2004)
McFee, B., Lanckriet, G.: Metric learning to rank. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010) (June 2010)
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7 (October 2007)
O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4, 344–377 (2004)
Pan, J., Winchester, D., Land, L., Watters, P.: Descriptive data mining on fraudulent online dating profiles. In: Proceedings of the 18th ECIS (2010)
Pandit, S., Chau, D.H., Wang, S., Faloutsos, C.: Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th WWW, pp. 201–210. ACM, New York (2007)
Pantel, P., Lin, D.: SpamCop: A Spam Classification and Organization Program. In: Workshop on Learning for Text Categorization (1998)
Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J.: Recon: a reciprocal recommender for online dating. In: RecSys 2010: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 207–214. ACM, New York (2010)
Sarawagi, S., Bhamidipaty, A.: Interactive deduplication using active learning. In: Proceedings of the 8th ACM SIGKDD, New York, pp. 269–278 (2002)
Toma, C.L., Hancock, J.T., Ellison, N.B.: Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles. Personality and Social Psychology Bulletin 34(8), 1023–1036 (2008)
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Pizzato, L.A., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., Kay, J. (2012). The Effect of Suspicious Profiles on People Recommenders. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds) User Modeling, Adaptation, and Personalization. UMAP 2012. Lecture Notes in Computer Science, vol 7379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31454-4_19
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DOI: https://doi.org/10.1007/978-3-642-31454-4_19
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