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- research-articleJanuary 2020
Cognitive-Based Hybrid Collaborative Filtering with Rating Scaling on Entropy to Defend Shilling Influence
ICNCC '19: Proceedings of the 2019 8th International Conference on Networks, Communication and ComputingPages 176–185https://doi.org/10.1145/3375998.3376040In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded ...
- tutorialOctober 2011
Robustness of recommender systems
RecSys '11: Proceedings of the fifth ACM conference on Recommender systemsPages 9–10https://doi.org/10.1145/2043932.2043937The possibility of designing user rating profiles to deliberately and maliciously manipulate the recommendation output of a collaborative filtering system was first raised in 2002. One scenario proposed was that an author, motivated to increase ...
- posterOctober 2010
Assessing trust in uncertain information using Bayesian description logic
CCS '10: Proceedings of the 17th ACM conference on Computer and communications securityPages 675–677https://doi.org/10.1145/1866307.1866394Decision makers (humans or software agents alike) are faced with the challenge of examining large volumes of information originating from heterogeneous sources with the goal of ascertaining trust in various pieces of information. In this paper we argue (...
- posterSeptember 2010
Merging multiple criteria to identify suspicious reviews
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsPages 241–244https://doi.org/10.1145/1864708.1864757Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor, given the rewards that can be derived from posting false or biased reviews. In this paper we present a number of criteria that might be ...
- research-articleJuly 2010
Distortion as a validation criterion in the identification of suspicious reviews
SOMA '10: Proceedings of the First Workshop on Social Media AnalyticsPages 10–13https://doi.org/10.1145/1964858.1964860Assessing the trustworthiness of reviews is a key issue for the maintainers of opinion sites such as TripAdvisor. In this paper we propose a distortion criterion for assessing the impact of methods for uncovering suspicious hotel reviews in TripAdvisor. ...
- ArticleDecember 2009
Average Shilling Attack against Trust-Based Recommender Systems
ICIII '09: Proceedings of the 2009 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 04Pages 588–591https://doi.org/10.1109/ICIII.2009.601Collaborative Filtering (CF) is considered a powerful technique for generating personalized recommendation. However, significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Malicious users can inject a ...
- articleNovember 2008
Manipulation-resistant recommender systems through influence limits
ACM SIGecom Exchanges (SIGECOM), Volume 7, Issue 3Article No.: 10, Pages 1–4https://doi.org/10.1145/1486877.1486887In this letter, we outline a new approach to modeling, analyzing, and combating manipulative attacks on recommender systems.
- research-articleOctober 2008
Unsupervised retrieval of attack profiles in collaborative recommender systems
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systemsPages 155–162https://doi.org/10.1145/1454008.1454034Trust, reputation and recommendation are key components of successful e-commerce systems. However, e-commerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation ...
- research-articleOctober 2008
The information cost of manipulation-resistance in recommender systems
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systemsPages 147–154https://doi.org/10.1145/1454008.1454033Attackers may seek to manipulate recommender systems in order to promote or suppress certain items. Existing defenses based on analysis of ratings also discard useful information from honest raters. In this paper, we show that this is unavoidable and ...
- research-articleJuly 2008
Attack resistant collaborative filtering
SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrievalPages 75–82https://doi.org/10.1145/1390334.1390350The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. ...
- ArticleOctober 2007
The influence limiter: provably manipulation-resistant recommender systems
RecSys '07: Proceedings of the 2007 ACM conference on Recommender systemsPages 25–32https://doi.org/10.1145/1297231.1297236An attacker can draw attention to items that don't deserve that attention by manipulating recommender systems. We describe an influence-limiting algorithm that can turn existing recommender systems into manipulation-resistant systems. Honest reporting ...
- articleOctober 2007
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT), Volume 7, Issue 4Pages 23–eshttps://doi.org/10.1145/1278366.1278372Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to “adapt” in a ...
- ArticleNovember 2005
An auctioning reputation system based on anomaly
- Shai Rubin,
- Mihai Christodorescu,
- Vinod Ganapathy,
- Jonathon T. Giffin,
- Louis Kruger,
- Hao Wang,
- Nicholas Kidd
CCS '05: Proceedings of the 12th ACM conference on Computer and communications securityPages 270–279https://doi.org/10.1145/1102120.1102156Existing reputation systems used by online auction houses do not address the concern of a buyer shopping for commodities - finding a good bargain. These systems do not provide information on the practices adopted by sellers to ensure profitable ...
- ArticleMay 2004
Shilling recommender systems for fun and profit
WWW '04: Proceedings of the 13th international conference on World Wide WebPages 393–402https://doi.org/10.1145/988672.988726Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items ...
- ArticleSeptember 2003
Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions
ICEC '03: Proceedings of the 5th international conference on Electronic commercePages 259–265https://doi.org/10.1145/948005.948040Online auctions allow the seller to remain anonymous and to easily change identities. Buyers must rely on the seller's description of a product and ability to deliver the product as promised. Internet auction environments make opportunistic behavior ...