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The Effect of Suspicious Profiles on People Recommenders

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7379)

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.

Keywords

  • Recommender System
  • Close Match
  • Collaborative Filter
  • Recommender Algorithm
  • Legitimate User

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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)

    Google Scholar 

  2. Brožovský, L., Petříček, V.: Recommender system for online dating service. CoRR abs/cs/0703042 (2007)

    Google Scholar 

  3. 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)

    CrossRef  Google Scholar 

  4. 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)

    CrossRef  Google Scholar 

  5. 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)

    Google Scholar 

  6. Hernández, M.A., Stolfo, S.J.: The merge/purge problem for large databases. SIGMOD Rec. 24, 127–138 (1995)

    CrossRef  Google Scholar 

  7. 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)

    CrossRef  Google Scholar 

  8. 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)

    Google Scholar 

  9. McFee, B., Lanckriet, G.: Metric learning to rank. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010) (June 2010)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4, 344–377 (2004)

    CrossRef  Google Scholar 

  12. Pan, J., Winchester, D., Land, L., Watters, P.: Descriptive data mining on fraudulent online dating profiles. In: Proceedings of the 18th ECIS (2010)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Pantel, P., Lin, D.: SpamCop: A Spam Classification and Organization Program. In: Workshop on Learning for Text Categorization (1998)

    Google Scholar 

  15. 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)

    CrossRef  Google Scholar 

  16. Sarawagi, S., Bhamidipaty, A.: Interactive deduplication using active learning. In: Proceedings of the 8th ACM SIGKDD, New York, pp. 269–278 (2002)

    Google Scholar 

  17. 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)

    CrossRef  Google Scholar 

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31453-7

  • Online ISBN: 978-3-642-31454-4

  • eBook Packages: Computer ScienceComputer Science (R0)