Robust Features for Trustable Aggregation of Online Ratings

Oh, Hyun-Kyo; Kim, Sang–Wook

doi:10.1145/2857546.2857560

Cited by 1 publication

(2 citation statements)

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“…To analyze the features of the similarities, we calculated the mean of the DegSim [11] using Pearson correlation coefficient, adjusted cosine similarity, cosine, and relevance-based similarity. This is because DegSim has been identified to be effective for fake users' detection [10,28]. In a typical DegSim, the k most similar neighbors are used to calculate the mean Pearson correlation coefficient similarity for each user (Eq.…”

Section: Analysis On Degsim Based On Different Similaritiesmentioning

confidence: 99%

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From similarity perspective: a robust collaborative filtering approach for service recommendations

Gao

Lü

Yang

et al. 2019

Front. Comput. Sci.

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Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions. We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.

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Section: Analysis On Degsim Based On Different Similaritiesmentioning

confidence: 99%

“…3. This could be the reason why researchers use Prs_DegSim as well as RDMA or other features [22,28] to detect fake users.…”

Section: Analysis On Degsim Based On Different Similaritiesmentioning

confidence: 99%