Abstract
Online recommendation services, such as e-commerce sites, rely on a vast amount of knowledge about users/items that represent an invaluable resource. Part of this acquired knowledge is public and can be accessed by anyone through the Internet. Unfortunately, that same knowledge can be used by competitors or malicious users. A large body of research proposes methods to attack recommender systems, but most of these works assume that the attacker knows or can easily access the rating matrix. In practice, this information is not directly accessible, but can only be gathered via crawling.
Considering such real-life limitations, in this paper, we assess the impact of different crawling approaches when attacking a recommendation service. From the crawled information, we mount different shilling attacks. We determine the value of the collected knowledge through the reconstruction of the user/item neighborhood. Our results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction), this will not be enough to mount a successful shilling attack in practice.
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References
Baeza-Yates, R., Castillo, C., Marin, M., Rodriguez, A.: Crawling a country: better strategies than breadth-first for web page ordering. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, WWW 2005, New York, NY, USA, pp. 864–872. Association for Computing Machinery (2005). https://doi.org/10.1145/1062745.1062768
Bhebe, W., Kogeda, O.P.: Shilling attack detection in collaborative recommender systems using a meta learning strategy. In: 2015 International Conference on Emerging Trends in Networks and Computer Communications, pp. 56–61 (2015)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the Seventh International Conference on World Wide Web, WWW 2007, pp. 107–117. Elsevier, NLD (1998)
Burke, R., Mobasher, B., Bhaumik, R.: Limited knowledge shilling attacks in collaborative filtering systems. In: Proceedings of the 3rd IJCAI Workshop in Intelligent Techniques for Personalization (2005)
Chakrabarti, S.: Focused Web Crawling, pp. 1147–1155. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_165
Chakrabarti, S., Dom, B., Raghavan, P., Rajagopalan, S., Gibson, D., Kleinberg, J.: Automatic resource compilation by analyzing hyperlink structure and associated text. In: Proceedings of the Seventh International Conference on World Wide Web 2007, WWW 2007, pp. 65–74. Elsevier, NLD (1998)
Cho, J., Garcia-Molina, H., Page, L.: Efficient crawling through URL ordering. Comput. Netw. ISDN Syst. 30(1), 161–172 (1998). https://doi.org/10.1016/S0169-7552(98)00108-1. http://www.sciencedirect.com/science/article/pii/S0169755298001081. Proceedings of the Seventh International World Wide Web Conference
Christakopoulou, K., Banerjee, A.: Adversarial attacks on an oblivious recommender. In: Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, pp. 322–330. ACM (2019). https://doi.org/10.1145/3298689.3347031. http://doi.acm.org/10.1145/3298689.3347031
Deldjoo, Y., Di Noia, T., Merra, F.A.: Assessing the impact of a user-item collaborative attack on class of users. In: In Proceedings of the 13th ACM RecSys Workshop on Impact of Recommender Systems (ImpactRS@RecSys 2019) (2019). http://sisinflab.poliba.it/publications/2019/DDM19
Eksombatchai, C., et al.: Pixie: a system for recommending 3+ billion items to 200+ million users in real-time. In: Proceedings of the 2018 World Wide Web Conference, WWW 2018, pp. 1775–1784. WWW Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https://doi.org/10.1145/3178876.3186183
Ester, M., Kriegel, H.P., Schubert, M.: Accurate and efficient crawling for relevant websites. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases - Volume 30, VLDB 2004, pp. 396–407. VLDB Endowment (2004)
Fang, M., Yang, G., Gong, N.Z., Liu, J.: Poisoning attacks to graph-based recommender systems. In: Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, New York, NY, USA, pp. 381–392. Association for Computing Machinery (2018). https://doi.org/10.1145/3274694.3274706
Gomez-Uribe, C., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6(4) (2016). https://doi.org/10.1145/2843948
Gunes, I., Bilge, A., Polat, H.: Shilling attacks against memory-based privacy-preserving recommendation algorithms. TIIS 7, 1272–1290 (2013)
Gunes, I., Kaleli, C., Bilge, A., Polat, H.: Shilling attacks against recommender systems: a comprehensive survey. Artif. Intell. Rev. 42(4), 767–799 (2012). https://doi.org/10.1007/s10462-012-9364-9
Hara, K., Suzuki, I., Kobayashi, K., Fukumizu, K.: Reducing hubness: a cause of vulnerability in recommender systems. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015, New York, NY, USA, pp. 815–818. Association for Computing Machinery (2015). https://doi.org/10.1145/2766462.2767823
Holzmann, H., Anand, A., Khosla, M.: Delusive PageRank in incomplete graphs. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 812, pp. 104–117. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05411-3_9
Holzmann, H., Anand, A., Khosla, M.: Estimating PageRank deviations in crawled graphs. Appl. Netw. Sci. 4, 86–107 (2019)
Hurley, N.J., O’Mahony, M.P., Silvestre, G.C.M.: Attacking recommender systems: a cost-benefit analysis. IEEE Intell. Syst. 22(3), 64–68 (2007)
Knees, P., Schnitzer, D., Flexer, A.: Improving neighborhood-based collaborative filtering by reducing hubness. In: Proceedings of International Conference on Multimedia Retrieval, ICMR 2014, New York, NY, USA, pp. 161–168. Association for Computing Machinery (2014). https://doi.org/10.1145/2578726.2578747
Koren, Y., Bell, R.: Advances in Collaborative Filtering, pp. 145–186. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_5
Koster, M.: Robots in the web: threat or treat? ConneXions 9(4), 8–18 (1995)
Lawankar, A., Mangrulkar, N.: A review on techniques for optimizing web crawler results. In: 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), pp. 1–4 (2016)
Li, B., Wang, Y., Singh, A., Vorobeychik, Y.: Data poisoning attacks on factorization-based collaborative filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 1893–1901 (2016). http://dl.acm.org/citation.cfm?id=3157096.3157308
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Muñoz-González, L., Pfitzner, B., Russo, M., Carnerero-Cano, J., Lupu, E.C.: Poisoning attacks with generative adversarial nets. ArXiv abs/1906.07773 (2019)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. In: WWW 1999 (1999)
Patel, K., Thakkar, A., Shah, C., Makvana, K.: A state of art survey on shilling attack in collaborative filtering based recommendation system. In: Satapathy, S.C.C., Das, S. (eds.) Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. SIST, vol. 50, pp. 377–385. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30933-0_38
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, Arlington, Virginia, USA, pp. 452–461. AUAI Press (2009)
Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_25
Si, M., Li, Q.: Shilling attacks against collaborative recommender systems: a review. Artif. Intell. Rev. 53(1), 291–319 (2018). https://doi.org/10.1007/s10462-018-9655-x
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009 (2009). https://doi.org/10.1155/2009/421425
Zhang, Y., Gao, H., Pei, G., Luo, S., Chang, G., Cheng, N.: A survey of research on captcha designing and breaking techniques. In: 2019 18th IEEE International Conference On Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 75–84 (2019)
Zhou, W., et al.: Shilling attacks detection in recommender systems based on target item analysis. PLoS ONE 10(7), 1–26 (2015). https://doi.org/10.1371/journal.pone.0130968
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This work was supported by the European Commission under the Horizon 2020 Programme (H2020), as part of the LOCARD project (Grant Agreement no. 832735).
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Appendices
A Neighborhood Reconstruction: User-Based with Cosine Similarity
In Fig. 7, we depict the results for all four considered datasets for the neighborhood reconstruction when using user-based cosine similarity.
B Neighborhood Reconstruction: Item-Based with Pearson’s Correlation
Finally, in Fig. 8, we depict the results for the neighborhood reconstruction when using an item-based Pearson correlation.
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Aiolli, F., Conti, M., Picek, S., Polato, M. (2020). Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. In: Chen, L., Li, N., Liang, K., Schneider, S. (eds) Computer Security – ESORICS 2020. ESORICS 2020. Lecture Notes in Computer Science(), vol 12309. Springer, Cham. https://doi.org/10.1007/978-3-030-59013-0_9
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