Robust Reputation-Based Ranking on Bipartite Rating Networks

Li, Ronghua; Yu, Jeffrey Xu; Huang, Xin; Cheng, Hong

doi:10.1137/1.9781611972825.53

Cited by 65 publications

(62 citation statements)

References 30 publications

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“…In addition, the algorithm suffers when the number of spamming users is relatively large. To address the convergence and spamming issue, Li et al develop six reputation-based algorithms in [18], which is mostly related to this work. In this paper, both L 1 and L 2 distances between item ranking and user rating, and three difference aggregation approaches (i.e., average, maximum, and minimum) are employed to estimate UR.…”

Section: Reputation-based Algorithms For Rating Systemsmentioning

confidence: 99%

“…With various divergence measurement functions, we can develop different algorithms. For instance, Kerchove et al [8] employ mean square errors, and Li et al [18] further explore L 1 and L 2 distances to measure the differences. However, all the above reputation-based algorithms do not consider UR as topic-biased and only employ a single value to model UR, without taking into account UR across different topics.…”

Section: Reputation-based Algorithms For Rating Systemsmentioning

confidence: 99%

“…Since the algorithms in [18] have great advantages than other reputation-based algorithms in convergence and robustness, we adopt them as sample algorithms, as shown in Table 3. For instance, if D k (·) employs the average L 1 distance, the iterative system of TB-L 1 -AVG algorithm is:…”

Section: Topic-biased Model and Algorithmsmentioning

confidence: 99%

“…To minimize the influence of spammers' ratings, user reputation has been investigated in rating systems. In this connection, a number of studies have proposed reputation-based algorithms [8,16,18,30,31] to estimate user reputation in rating systems. Although there is no formal definition for user reputation, a running definition in rating systems is given below: Definition 1.1 (User reputation (UR)) In a rating system, user reputation evaluates a user's ability to give reliable, fair, and trustworthy ratings.…”

Section: Introductionmentioning

confidence: 99%

“…Consider the following case shown in Table 1, where five users give ratings to six items, together with these items' "true" scores. We estimate UR for the five users using a state-of-the-art reputation-based algorithm L1-AVG [18]. The results are 0.80, 0.84, 0.92, 0.90, and 0.92 (UR is normalized to [0,1], the higher, the better).…”

Section: Introductionmentioning

confidence: 99%

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A topic-biased user reputation model in rating systems

Bai-chuan

King

et al. 2014

Knowl Inf Syst

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In rating systems like Epinions and Amazon's product review systems, users rate items on different topics to yield item scores. Traditionally, item scores are estimated by averaging all the ratings with equal weights. To improve the accuracy of estimated item scores, user reputation [a.k.a., user reputation (UR)] is incorporated. The existing algorithms on UR, however, have underplayed the role of topics in rating systems. In this paper, we first reveal that UR is topic-biased from our empirical investigation. However, existing algorithms cannot capture this characteristic in rating systems. To address this issue, we propose a topic-biased model (TBM) to estimate UR in terms of different topics as well as item scores. With TBM, we develop six topic-biased algorithms, which are subsequently evaluated with experiments using both real-world and synthetic data sets. Results of the experiments demonstrate that the topic-biased algorithms effectively estimate UR across different topics and produce more robust item scores than previous reputation-based algorithms, leading to potentially more robust rating systems.

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Section: Reputation-based Algorithms For Rating Systemsmentioning

confidence: 99%

Section: Reputation-based Algorithms For Rating Systemsmentioning

confidence: 99%

Section: Topic-biased Model and Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A topic-biased user reputation model in rating systems

Bai-chuan

King

et al. 2014

Knowl Inf Syst

Self Cite

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Web service quality of service prediction via regional reputation‐based matrix factorization

Ghafouri

Hashemi

Razzazi

et al. 2021

Concurrency and Computation

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Quality of Service (QoS) of Web services plays an essential role in selecting Web services by consumers. The dynamic QoS attributes of Web services have different values for different users. Therefore, the value of many Web services' QoS features for many users are undetermined, and these values should be predicted. The collaborative filtering (CF) method is one of the most successful approaches to predict these values. CF‐based methods use the QoS values contributed by the other users for prediction and, consequently, the values contributed by unreliable users can decrease the accuracy of prediction. To utilize the reputation of users can be regarded as one of the conventional approaches to overcome this problem. In this paper, we have defined a concept called regional reputation that represents the reputation of a user for users in each geographical region. Regional reputation has been achieved with the combination of the location information of the users and their reputation. Subsequently, by combining this concept with the matrix factorization, we have proposed a prediction method called regional reputation‐based matrix factorization. This approach has been able to improve the accuracy of prediction and be more persistent to the data contributed by unreliable users.

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A new model for trustworthy web service QoS prediction

Hashemi

Ghafouri

Hung

2021

Concurrency and Computation

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The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

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Robust Reputation-Based Ranking on Bipartite Rating Networks

Cited by 65 publications

References 30 publications

A topic-biased user reputation model in rating systems

A topic-biased user reputation model in rating systems

Web service quality of service prediction via regional reputation‐based matrix factorization

A new model for trustworthy web service QoS prediction

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