Correcting noisy ratings in collaborative recommender systems

Toledo, Raciel Yera; Mota, Yailé Caballero; Martínez, Luis

doi:10.1016/j.knosys.2014.12.011

Cited by 74 publications

(53 citation statements)

References 44 publications

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“…The proposal depends on three parameters: κ, ν and δ. These parameters are highly domain-dependant in [10] can be found a further detailed analysis about them.…”

Section: Mark the Rating As Possible Noise Ifmentioning

confidence: 91%

“…But, the necessity of customers preferences in CFRSs has produced some problems that limit their performance, such as cold start and sparsity [1, ? ], and more recently new related problems regarding the quality of the rating data have raised up [8][9][10]. Specifically, Ekstrand et al [11] pointed out that the rating elicitation process is not error-free, hence the ratings can contain noise.…”

Section: Martin@ujaenesmentioning

confidence: 99%

“…The management of NN depends on the data available and some approaches require additional user interactions [9] and others just remove noisy data either ratings or users [13]. Recently, however, new proposals manage the NN by using the current rating values in the user/item rating matrix while keeping as much information as possible and without any new user's interaction [10,14].…”

Section: Martin@ujaenesmentioning

confidence: 99%

“…Yera, Caballero and Martínez [10] proposed a method for correcting noisy preferences that only relies on the ratings and it processes them at a rating level in order to fill the gray cell in Table 2. This approach takes into account just rating values, in which it was not necessary to use any additional information about users either items.…”

Section: Natural Noise In Recommender Systemsmentioning

confidence: 99%

“…Yera, Caballero and Martínez [10] proposal adopted the latter view. Before correction, another reason that classifies ratings as noise is verified.…”

Section: Noise Correction Processmentioning

confidence: 99%

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Managing Natural Noise in Recommender Systems

Martínez

Castro

Toledo

2016

Theory and Practice of Natural Computing

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Abstract. E-commerce customers demand quick and easy access to suitable products in large purchase spaces. To support and facilitate this purchasing process to users, recommender systems (RSs) help them to find out the information that best fits their preferences and needs in an overloaded search space. These systems require the elicitation of customers' preferences. However, this elicitation process is not always precise either correct because of external factors such as human errors, uncertainty, human beings inherent inconsistency and so on. Such a problem in RSs is known as natural noise (NN) and can negatively bias recommendations, which leads to poor user's experience. Different proposals have been presented to deal with natural noise in RSs. Several of them require additional interaction with customers. Others just remove noisy information. Recently, new NN approaches dealing with the ratings stored in the user/item rating matrix have raised to deal with NN in a better and simpler way. This contribution is devoted to provide a brief review of the latter approaches revising crisp and fuzzy approaches for dealing with NN in RSs. Eventually it points out as a future research the management of NN in other recommendation scenarios as group RSs.

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“…The proposal depends on three parameters: κ, ν and δ. These parameters are highly domain-dependant in [10] can be found a further detailed analysis about them.…”

Section: Mark the Rating As Possible Noise Ifmentioning

confidence: 91%

Section: Martin@ujaenesmentioning

confidence: 99%

Section: Martin@ujaenesmentioning

confidence: 99%

Section: Natural Noise In Recommender Systemsmentioning

confidence: 99%

“…Yera, Caballero and Martínez [10] proposal adopted the latter view. Before correction, another reason that classifies ratings as noise is verified.…”

Section: Noise Correction Processmentioning

confidence: 99%

See 3 more Smart Citations

Managing Natural Noise in Recommender Systems

Martínez

Castro

Toledo

2016

Theory and Practice of Natural Computing

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A fuzzy clustering‐based denoising model for evaluating uncertainty in collaborative filtering recommender systems

Zhu

Han

Gou

et al. 2018

Asso for Info Science & Tech

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Recommender systems are effective in predicting the most suitable products for users, such as movies and books. To facilitate personalized recommendations, the quality of item ratings should be guaranteed. However, a few ratings might not be accurate enough due to the uncertainty of user behavior and are referred to as natural noise. In this article, we present a novel fuzzy clustering‐based method for detecting noisy ratings. The entropy of a subset of the original ratings dataset is used to indicate the data‐driven uncertainty, and evaluation metrics are adopted to represent the prediction‐driven uncertainty. After the repetition of resampling and the execution of a recommendation algorithm, the entropy and evaluation metrics vectors are obtained and are empirically categorized to identify the proportion of the potential noise. Then, the fuzzy C‐means‐based denoising (FCMD) algorithm is performed to verify the natural noise under the assumption that natural noise is primarily the result of the exceptional behavior of users. Finally, a case study is performed using two real‐world datasets. The experimental results show that our proposal outperforms previous proposals and has an advantage in dealing with natural noise.

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