Abstract
Digital content production and distribution has radically changed our business models. An unprecedented volume of supply is now on offer, whetted by the demand of millions of users from all over the world. Since users cannot be expected to browse through millions of different items to find what they might like, filtering has become a popular technique to connect supply and demand: trusted users are first identified, and their opinions are then used to create recommendations. In this domain, users' trustworthiness has been measured according to one of the following two criteria: taste similarity (i.e., “I trust those who agree with me”), or social ties (i.e., “I trust my friends, and the people that my friends trust”). The former criterion aims at identifying concordant users, but is subject to abuse by malicious behaviors. The latter aims at detecting well-intentioned users, but fails to capture the natural subjectivity of tastes. In this article, we propose a new definition of trusted recommenders, addressing those users that are both well-intentioned and concordant. Based on this characterisation, we propose a novel approach to information filtering that we call dependable filtering. We describe alternative algorithms realizing this approach, and demonstrate, by means of extensive performance evaluation on a variety of real large-scale datasets, the high degree of both accuracy and robustness they entail.
- Aggarwal, C. C., Wolf, J. L., Wu, K.-L., and Yu, P. S. 1999. Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 201--212. Google ScholarDigital Library
- Anderson, C. 2006. The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion. Google ScholarDigital Library
- Avesani, P., Massa, P., and Tiella, R. 2005. A trust-enhanced recommender system application: Moleskiing. In Proceedings of the ACM Symposium on Applied Computing. ACM, New York, 1589--1593. Google ScholarDigital Library
- Axelrod, R. 1984. The Evolution of Cooperation. Basic Books, New York.Google Scholar
- Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., Zhang, C., and Ross, K. 2008. Identifying video spammers in online social networks. In Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web. ACM, New York, 45--52. Google ScholarDigital Library
- Bhaumik, R., Burke, R., and Mobasher., B. 2007. Effectiveness of crawling attacks against web-based recommender systems. In Proceedings of the AAAI Workshop on Intelligent Techniques for Web Personalization. 17--26.Google Scholar
- Bhaumik, R., Williams, C., Mobasher, B., and Burke, R. 2006. Securing collaborative filtering against malicious attacks through anomaly detection. In Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization.Google Scholar
- Bonhard, P. and Sasse, M. A. 2006. ‘Knowing me, knowing you’—using profiles and social networking to improve recommender systems. BT Technol. J. 24, 3, 84--98. Google ScholarDigital Library
- Burke, R., Mobasher, B., Bhaumik, R., and Williams, C. 2005. Segment-Based injection attacks against collaborative filtering recommender systems. In Proceedings of the 5th IEEE International Conference on Data Mining. Google ScholarDigital Library
- Chen, H. and Ng, T. 1999. An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation): Symbolic branch-and-bound search vs. connectionist Hopfield net activation. J. Amer. Soc. Inform. Sci. 46, 5, 348--369. Google ScholarDigital Library
- Cheng, A. and Friedman, E. 2005. Sybilproof reputation mechanisms. In Proceedings of the 3rd Workshop on Economics of Peer-to-Peer Systems (P2PECON'05). Google ScholarDigital Library
- Chirita, P.-A., Nejdl, W., and Zamfir, C. 2005. Preventing shilling attacks in online recommender systems. In Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management. 67--74. Google ScholarDigital Library
- Dell'Amico, M. and Capra, L. 2008. SOFIA: Social filtering for robust recommendations. In Proceedings of the 2nd Joint iTrust and PST Conferences on Privacy, Trust Management and Security. 135--150.Google Scholar
- Deshpande, M. and Karypis, G. 2004. Item-Based top-n recommendation algorithms. ACM Trans. Inform. Syst. 22, 1, 177. Google ScholarDigital Library
- Douceur, J. R. 2002. The Sybil attack. In Proceedings of the 1st International Workshop on Peer-to-Peer Systems (IPTPS'02). Google ScholarDigital Library
- Feldman, M., Lai, K., Stoica, I., and Chuang, J. 2004. Robust incentive techniques for peer-to-peer networks. In Proceedings of the ACM Conference on Electronic Commerce (EC'04). Google ScholarDigital Library
- Fogaras, D., Racz, B., Csalogany, K., and Sarlos, T. 2005. Towards scaling fully personalized pagerank: Algorithms, lower bounds, and experiments. Internet Math. 2, 3.Google ScholarCross Ref
- Friedman, E. J. and Cheng, A. 2006. Manipulability of PageRank under Sybil strategies. In Proceedings of the 1st Workshop on Networked Systems (NetEcon'06).Google Scholar
- Gleich, D. and Polito, M. 2006. Approximating personalized pagerank with minimal use of webgraph data. Internet Math. 3, 3, 257--294.Google ScholarCross Ref
- Golbeck, J. 2005. Computing and applying trust in web-based social networks. Ph.D. thesis, University of Maryland. Google ScholarDigital Library
- Golbeck, J. 2008. Weaving a Web of Trust. Sci., 1640--1641.Google Scholar
- Groh, G. and Ehmig, C. 2007. Recommendations in taste related domains: Collaborative filtering vs. social filtering. In Proceedings of the International ACM Conference on Supporting Group Work. 127--136. Google ScholarDigital Library
- Guha, R., Kumar, R., Raghavan, P., and Tomkins, A. 2004. Propagation of trust and distrust. In Proceedings of the 13th International Conference on World Wide Web (WWW'04). 403--412. Google ScholarDigital Library
- Gupta, M., Judge, P., and Ammar, M. 2003. A reputation system for peer-to-peer networks. In Proceedings of the 13th International Workshop on Network and Operating Systems Support for Digital Audio and Video. 144--152. Google ScholarDigital Library
- Gupta, M., Pathak, A., and Chakrabarti, S. 2008. Fast algorithms for top-k personalized pagerank queries. In Proceedings of the 17th International Conference on World Wide Web. 1225--1226. Google ScholarDigital Library
- Haveliwala, T., Kamvar, A., and Jeh, G. 2003. An analytical comparison of approaches to personalizing Pagerank. Tech. rep., Stanford InfoLab.Google Scholar
- Haveliwala, T. and Kamvar, S. 2003. The second eigenvalue of the google matrix. Tech. rep. 20/2003, Stanford University.Google Scholar
- Haveliwala, T. H. 2003. Topic-Sensitive PageRank: A context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Engin. 15. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International Conference on Research and Development in Information Retrieval (SIGIR'99). ACM Press, New York, 230--237. Google ScholarDigital Library
- Hu, Y., Koren, Y., and Volinsky, C. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08). IEEE Computer Society, Los Alamitos, CA, 263--272. Google ScholarDigital Library
- Huang, Z., Chen, H., and Zeng, D. 2004. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inform. Syst. 22, 1, 116--142. Google ScholarDigital Library
- Huang, Z., Li, X., and Chen, H. 2005. Link prediction approach to collaborative filtering. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital libraries. 141--142. Google ScholarDigital Library
- Huang, Z. and Zeng, D. 2005. Why does collaborative filtering work?—Recommendation model validation and selection by analyzing random bipartite graphs. In Proceedings of the 15th Annual Workshop on Information Technologies and Systems. 33--38.Google Scholar
- Huang, Z., Zeng, D. D., and Chen, H. 2007. Analyzing consumer-product graphs: Empirical findings and applications in recommender systems. Manag. Sci. 53, 7, 1146--1164. Google ScholarDigital Library
- Hurley, N. J., O'Mahony, M. P., and Silvestre, G. C. M. 2007. Attacking recommender systems: A cost-benefit analysis. IEEE Intell. Syst. 22, 3, 64--68. Google ScholarDigital Library
- Jeh, G. and Widom, J. 2003. Scaling personalized web search. In Proceedings of the 12th International Conference on World Wide Web. 271--279. Google ScholarDigital Library
- Jösang, A. 1996. The right type of trust for distributed systems. In Proceedings of the Workshop on New Security Paradigms (NSPW'96). ACM Press, 119--131. Google ScholarDigital Library
- Kamvar, S. D., Schlosser, M. T., and Garcia-Molina, H. 2003. The EigenTrust algorithm for reputation management in P2P networks. In Proceedings of the International Conference on World Wide Web (WWW'03). 640--651. Google ScholarDigital Library
- Kautz, H., Selman, B., and Shah, M. 1997. Referral web: Combining social networks and collaborative filtering. Comm. ACM 40, 3, 63--65. Google ScholarDigital Library
- Kleinberg, J. M. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 604--632. Google ScholarDigital Library
- Kofman, I. and Rajaraman, S. 2010. New video page launches for all users. http://youtube-global. blogspot.com/2010/03/new-video-page-launches-for-all-users.html.Google Scholar
- Kruk, S. 2004. FOAF-Realm-Control your friends' access to the resource. In Proceedings of the FOAF Workshop.Google Scholar
- Lam, S., Frankowski, D., and Riedl, J. 2006. Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. Emerg. Trends Inform. Comm. Secur., 14--29. Google ScholarDigital Library
- Lam, S. K. and Riedl, J. 2004. Shilling recommender systems for fun and profit. In Proceedings of the 13th International Conference on World Wide Web (WWW'04). ACM Press, 393--402. Google ScholarDigital Library
- Langheinrich, M. 2003. When trust does not compute—The role of trust in ubiquitous computing. In Proceedings of the Workshop on Privacy at Ubicomp 2003.Google Scholar
- Lathia, N., Hailes, S., and Capra, L. 2008a. kNN CF: A temporal social network. In Proceedings of the 2nd ACM International Conference on Recommender Systems. ACM Press. Google ScholarDigital Library
- Lathia, N., Hailes, S., and Capra, L. 2008b. The effect of correlation coefficients on communities of recommenders. In Proceedings of the 23rd Annual ACM Symposium on Applied Computing— Trust, Recommendations, Evidence and Other Collaboration Know-How (TRECK) Track. Google ScholarDigital Library
- Lempel, R. and Moran, S. 2001. Salsa: The stochastic approach for link-structure analysis. ACM Trans. Inform. Syst. 19, 2, 131--160. Google ScholarDigital Library
- Massa, P. and Avesani, P. 2007. Trust-Aware recommender systems. In Proceedings of ACM Recommender Systems Conference. Google ScholarDigital Library
- Mehta, B. and Hofmann, T. 2008. A survey of attack-resistant collaborative filtering algorithms. Bull. Techn. Comm. Data Engin. 31, 2, 14--22.Google Scholar
- Mobasher, B., Burke, R., Bhaumik, R., and Sandvig, J. J. 2007a. Attacks and remedies in collaborative recommendation. IEEE Intell. Syst. 22, 3, 56--63. Google ScholarDigital Library
- Mobasher, B., Burke, R., Bhaumik, R., and Williams, C. 2007b. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Inter. Tech. 7, 4, 23. Google ScholarDigital Library
- Mobasher, B., Burke, R., and Sandvig, J. J. 2006. Model-based collaborative filtering as a defense against profile injection attacks. In Proceedings of the 21st Conference on Artificial Intelligence (AAAI'06). Google ScholarDigital Library
- Nowak, M. A. and Sigmund, K. 1998. Evolution of indirect reciprocity by image scoring. Nature 393, 6685, 573--577.Google Scholar
- O'Donovan, J. and Smyth, B. 2006. Is trust robust?: An analysis of trust-based recommendation. In Proceedings of the 11th International Conference on Intelligent User Interfaces. 101--108. Google ScholarDigital Library
- O'Mahony, M. P., Hurley, N. J., and Silvestre, G. C. M. 2004. An evaluation of neighbourhood formation on the performance of collaborative filtering. Artif. Intell. Rev. 21, 3, 215--228. Google ScholarDigital Library
- O'Mahony, M., Hurley, N., and Silvestre, G. 2002. Promoting recommendations: An attack on collaborative filtering. In Database and Expert Systems Applications. Lecture Notes in Computer Science, vol. 2453. Springer, 213--241. Google ScholarDigital Library
- Page, L., Brin, S., Motwani, R., and Winograd, T. 1998. The PageRank citation ranking: Bringing order to the web. Tech. rep., Stanford Digital Library Technologies Project.Google Scholar
- Rajaraman, S. 2009. Five stars dominate ratings. http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html.Google Scholar
- Ray, S. and Mahanti, A. 2008. Strategies for effective shilling attacks against recommender systems. In Proceedings of the 2nd ACM SIGKDD International Workshop on Privacy, Security, and Trust. 111--125. Google ScholarDigital Library
- Resnick, P. and Sami, R. 2007. The influence limiter: Provably manipulation-resistant recommender systems. In Proceedings of the ACM Conference on Recommender Systems (RecSys'07). ACM, New York, 25--32. Google ScholarDigital Library
- Resnick, P. and Sami, R. 2008. The information cost of manipulation-resistance in recommender systems. In Proceedings of the ACM Conference on Recommender Systems (RecSys'08). ACM, New York, 147--154. Google ScholarDigital Library
- Sandvig, J., Mobasher, B., and Burke, R. 2007a. Impact of relevance measures on the robustness and accuracy of collaborative filtering. In Proceedings of the 8th International Conference on E-Commerce and Web Technologies (EC-Web'07). 99--108. Google ScholarDigital Library
- Sandvig, J. J., Mobasher, B., and Burke, R. 2007b. Robustness of collaborative recommendation based on association rule mining. In Proceedings of the ACM Conference on Recommender Systems. 105--112. Google ScholarDigital Library
- Sandvig, J. J., Mobasher, B., and Burke, R. 2008. A survey of collaborative recommendation and the robustness of model-based algorithms. Bull. Techn. Comm. Data Engin. 31, 2, 3--13.Google Scholar
- Schollmeier, R. 2001. A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications. In Proceedings of the 1st International Conference on Peer-to-Peer Computing. Google ScholarDigital Library
- Wang, J., de Vries, A., and Reinders, M. 2006. A user-item relevance model for log-based collaborative filtering. In Advances in Information Retrieval, M. Lalmas, A. MacFarlane, S. Rüger, A. Tombros, T. Tsikrika, and A. Yavlinsky, Eds., Vol. 3936. Springer, 37--48. Google ScholarDigital Library
- Williams, C., Mobasher, B., and Burke, R. 2007. Defending recommender systems: Detection of profile injection attacks. Service Orient. Comput. Appl. 1, 3, 157--170.Google ScholarCross Ref
- Yu, H., Kaminsky, M., Gibbons, P. B., and Flaxman, A. 2006. Sybilguard: defending against sybil attacks via social networks. In Proceedings of ACM SIGCOMM. 267--278. Google ScholarDigital Library
- Zhang, S., Chakrabarti, A., Ford, J., and Makedon, F. 2006. Attack detection in time series for recommender systems. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 809--814. Google ScholarDigital Library
- Zheng, R., Provost, F., and Ghose, A. 2007. Social network collaborative filtering. Tech. rep. CeDER-07-04, New York University. September.Google Scholar
Index Terms
- Dependable filtering: Philosophy and realizations
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