Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system

Nilashi, Mehrbakhsh; Ibrahim, Othman; Ithnin, Norafida

doi:10.1016/j.knosys.2014.01.006

Cited by 81 publications

(27 citation statements)

References 61 publications

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“…To compute the similarity between users, the Jaccard metric was used as a weighting scheme with the CPC to obtain a weighted CPC measure [22]. To deal with the disadvantage of the single-rating based approach, multicriteria collaborative filtering was developed [23].…”

Section: Collaborative Filtering-based Recommendation Techniquesmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,307

546

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A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobilebased platforms). Some significant new topics are identified and listed as new directions. By providing a stateof-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

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Section: Collaborative Filtering-based Recommendation Techniquesmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,307

546

View full text Add to dashboard Cite

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“…It is generally used for matrix diagonalization in matrix analysis. It is primarily used in signal processing applications, statistics, and other fields [16][17][18]. Because our fracture reduction robot has a special design, its TCS can be reached easily using the visual probe and the optical tracking system, which then directly calculate the hand-eye transformation matrix.…”

Section: Discussionmentioning

confidence: 99%

A new hand-eye calibration approach for fracture reduction robot

Wang

Tang

et al. 2017

Computer Assisted Surgery

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Objective: The hand-eye calibration is used to determine the transformation between the end-effector and the camera marker of the robot. But the robot movement in traditional method would be time-consuming, inaccurate and even unavailable in some conditions. The method presented in this article can complete the calibration without any movement and is more suitable in clinical applications. Methods: Instead of solving the classic non-linear equation AX ¼ XB, we collected the points on X and Y axes of the tool coordinate system (TCS) with the visual probe and fitted them using the singular value decomposition algorithm (SVD). Then, the transformation was obtained with the data of the tool center point (TCP). A comparison test was conducted to verify the performance of the method. Results: The average translation error and orientation error of the new method are 0.12 ± 0.122 mm and 0.18 ± 0.112 respectively, while they are 0.357 ± 0.347 mm and 0.416 ± 0.234 correspondingly in the traditional method. Conclusions: The high accuracy of the method indicates that it is a good candidate for medical robots, which usually need to work in a sterile environment.

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“…The proposal was tested in the domain of retrieving technical papers. Afterwards, a more advanced approach were developed by Nilashi et al [91], that presents a new model for multi-criteria collaborative filtering using an Adaptive-Network-based Fuzzy Inference System [54] (ANFIS, see Fig. 17 ) combined with subtractive clustering and high order singular value decomposition.…”

Section: Maementioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

151

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system

Cited by 81 publications

References 61 publications

Recommender system application developments: A survey

Recommender system application developments: A survey

A new hand-eye calibration approach for fracture reduction robot

Fuzzy Tools in Recommender Systems: A Survey

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