An empirical study of natural noise management in group recommendation systems

Castro, Jorge; Toledo, Raciel Yera; Martínez, Luis

doi:10.1016/j.dss.2016.09.020

Cited by 44 publications

(16 citation statements)

References 17 publications

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“…Overall, these last results suggest that the use of techniques for preprocessing the data in the current POJ scenario can improve the recommendation accuracy, in a similar way to other preprocessing approaches for recommendation scenarios [37,38].…”

Section: Significance Valuesmentioning

confidence: 69%

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

2018

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Programming online judges (POJs) are an emerging application scenario in e-learning recommendation areas. Specifically, they are e-learning tools usually used in programming practices for the automatic evaluation of source code developed by students when they are solving programming problems. Usually, they contain a large collection of such problems, to be solved by students at their own personalized pace. The more problems in the POJ the harder the selection of the right problem to solve according to previous users performance, causing information overload and a widespread discouragement. This paper presents a recommendation framework to mitigate this issue by suggesting problems to solve in programming online judges, through the use of fuzzy tools which manage the uncertainty related to this scenario. The evaluation of the proposal uses real data obtained from a programming online judge, and shows that the new approach improves previous recommendation strategies which do not consider uncertainty management in the programming online judge scenarios. Specifically, the best results were obtained for short recommendation lists.

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Section: Significance Valuesmentioning

confidence: 69%

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

2018

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“…Future works will be focused on: 1) exploring the behavior of the proposal in the group recommendation scenario [37]; 2) incorporating the time dimension into the proposal, and 3) exploring its effect in the recommendation performance at more specific recommendation domains, such as an e-learning scenario [38].…”

Section: Discussionmentioning

confidence: 99%

Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation

Toledo¹,

Barranco²,

Alzahrani³

et al. 2019

IJCIS

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Recommender systems have played a relevant role in e-commerce for supporting online users to obtain suggestions about products that best fit their preferences and needs in overloaded search spaces. In such a context, several authors have proposed methods focused on removing the users' inconsistencies when they rate items, so-called natural noise, improving in this way the recommendation performance. The current paper explores the use of rating regularities for managing the natural noise in collaborative filtering recommendation, having as key feature the use of fuzzy techniques for coping with the uncertainty associated to such scenarios. Specifically, such regularities are used for representing common rating patterns and thus detect noisy ratings when they tend to contradict such patterns. An experimental study is developed for showing the performance of the proposal, as well as analyzing its behavior in contrast to previous natural noise management procedures.

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“…DSS has been widely used for production line management, for example, Gramajo and Martinez [11] applied the DSS model to manage the network traffic within the users and organizations using linguistic information, which recommends priorities for suppliers. Recommendations and decisions can be processed by rating a database through a decision tree in models [12]. Our primary motivation is to prevent mistakes from manual recording, to remotely monitor the working hours of employees in manufacturing processes via internet, and to evaluate the rationality of employee's working hours.…”

Section: Introductionmentioning

confidence: 99%

Developing a Decision Support System (DSS) for a Dental Manufacturing Production Line Based on Data Mining

Cheng¹,

Chen

Cheng³

et al. 2018

ASI

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In this study, an Industry 4.0 framework-based decision support system (DSS) was developed using a combination of wireless network and RFID identification technology to manage a production line in a traditional dental manufacturing lab. The motivation was to prevent mistakes from manual recording, to remotely monitor the working hours of employees in manufacturing processes via internet, and to evaluate the rationality of the employee's working hours. In the DSS, four network nodes were established to track four important manufacturing processes of digital dentistry. In each of these processes, the time spent by the dental technician was recorded by scanning their ID cards. All information was simultaneously uploaded to a databank in the cloud and analyzed by the computer software MATLAB. These programs evaluated the rationality of employees' working hours in each of the monitored processes, which can help managers to follow up or improve the process efficiency.

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An empirical study of natural noise management in group recommendation systems

Cited by 44 publications

References 17 publications

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

A Recommender System for Programming Online Judges Using Fuzzy Information Modeling

Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation

Developing a Decision Support System (DSS) for a Dental Manufacturing Production Line Based on Data Mining

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