A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office

Porcel, Carlos; Tejeda-Lorente, Álvaro; Martínez, María Angeles Aláez; Liu, Xinyue

doi:10.1016/j.ins.2011.08.026

Cited by 175 publications

(94 citation statements)

References 61 publications

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“…Many ensemble methods have been presented [21][22][23]; however, these methods do not suit this adaptive inspection system. This is because (1) these methods are sensitive to the size of the training sample set (although, even for a mature production line, there are not too many labeled samples), and (2) the fusion of classifiers may be biased from the combination of samples from the changed model.…”

Section: Combining Subclassifiers By Using the Bayes Kernelmentioning

confidence: 99%

An evolutionary classifier for steel surface defects with small sample set

Xiao

Jiang

et al. 2017

J Image Video Proc.

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Nowadays, surface defect detection systems for steel strip have replaced traditional artificial inspection systems, and automatic defect detection systems offer good performance when the sample set is large and the model is stable. However, the trained model does work well when a new production line is initiated with different equipment, processes, or detection devices. These variables make just tiny changes to the real-world model but have a significant impact on the classification result. To overcome these problems, we propose an evolutionary classifier with a Bayes kernel (BYEC) that can be adjusted with a small sample set to better adapt the model for a new production line. First, abundant features were introduced to cover detailed information about the defects. Second, we constructed a series of support vector machines (SVMs) with a random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel fused with the results from the sub-SVM to form an integrated classifier. Finally, we proposed a method to adjust the Bayes evolutionary kernel with a small sample set. We compared the performance of this method to various algorithms; experimental results demonstrate that the proposed method can be adjusted with a small sample set to fit the changed model. Experimental evaluations were conducted to demonstrate the robustness, low requirement for samples, and adaptiveness of the proposed method.

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Section: Combining Subclassifiers By Using the Bayes Kernelmentioning

confidence: 99%

An evolutionary classifier for steel surface defects with small sample set

Xiao

Jiang

et al. 2017

J Image Video Proc.

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“…[65], [21], [66], [67], [68], [69] [7], [175], [106], [8], [176], [177], [178], [179], [180], [181], [182], [57], [116] [187] , [190], [191], [75], [192], [31], [76], [193], [194] 5…”

Section: Model Based Techniquesunclassified

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Sohail

Siddiqui

Ali

2017

JESTR

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This paper presents the state of art techniques in recommender systems (RS). The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions. The review studies on RS are highlighted which helps in understanding the previous review works and their directions. 8 different main categories of recommender techniques and 19 sub categories have been identified and stated. Further, soft computing approach for recommendation is emphasized which have not been well studied earlier. The major problems of the existing area is reviewed and presented from different perspectives. However, solutions to these issues are rarely discussed in the previous works, in this study future direction for possible solutions are also addressed.

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“…Demographic filtering (Krulwich, 1997;Pazzani, 1999;Porcel, Tejeda-Lorente, Martínez, & Herrera-Viedma, 2012) is justified on the principle that individuals with certain common personal attributes (sex, age, country, etc. ) will also have common preferences.…”

Section: Content-based Filteringmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

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A hybrid recommender system for the selective dissemination of research resources in a Technology Transfer Office

Cited by 175 publications

References 61 publications

An evolutionary classifier for steel surface defects with small sample set

An evolutionary classifier for steel surface defects with small sample set

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Incorporating group recommendations to recommender systems: Alternatives and performance

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