An efficient parallel similarity matrix construction on MapReduce for collaborative filtering

Kim, Seung-Hee; Kim, Hong-Yeon; Min, Jun‐Ki

doi:10.1007/s11227-018-2271-3

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…Setting up School-Enterprise Cooperation Teaching Resource Bank. School-enterprise cooperation teaching resource platform is designed to overcome the boring of traditional learning mode, enhance the fun of learning process, meet the personalized learning needs of students, and facilitate teacher-student interaction [6]. e teaching resource platform is divided into five functional modules as follows: user information management, teaching resource management, resource statistical analysis, course management, and micro-lesson management, among which teaching resource management and course management are the core parts of the platform management [7].…”

Section: Design Of Personalized Recommendation Methods For Ideologica...mentioning

confidence: 99%

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Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode

Xia¹,

Ahmad

2022

Mathematical Problems in Engineering

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The personalized recommendation system influences the recommendation of ideological and political teaching resources in universities, resulting in a high MAE score. As a result, under the school-enterprise collaboration paradigm, this study proposes a customised recommendation approach for ideological and political teaching resources in colleges and universities. The ideological and political teaching resource bank is developed against the backdrop of the teaching paradigm that combines universities and businesses. Learners’ browsing data history is gathered to create a learning interest model for them. A hybrid collaborative filtering recommendation method was devised, and a recommendation engine was established by Taste component, taking into account individualised resource recommendation needs and information entropy weight distribution mode. When compared to previous techniques, the developed customised recommendation method considerably enhances the recommendation quality of instructional resources and reduces MAE by 29% and 34%, respectively.

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Section: Design Of Personalized Recommendation Methods For Ideologica...mentioning

confidence: 99%

“…Learning In formula (6), I represents information entropy, and η represents the probability of learning behavior k appearing in behavior records. Finally, according to the calculation formula of behavior information entropy, the information entropy value of each behavior can be calculated.…”

Section: Designing a Hybrid Collaborative Filteringmentioning

confidence: 99%

Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode

Xia¹,

Ahmad

2022

Mathematical Problems in Engineering

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“…Chinese folk music began its history of international dissemination more than two thousand years ago [20,21]. e dissemination of national music culture has changed from unconscious dissemination to conscious dissemination, meeting the needs of domestic and foreign music culture, and now actively conducting international dissemination has become one of the effective ways to enhance the country's soft power [22].…”

Section: Overview Of National Musicmentioning

confidence: 99%

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

Deng

2021

Security and Communication Networks

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Personalized music recommendations can accurately push the music of interest from a massive song library based on user information when the user’s listening needs are blurred. To this end, this paper proposes a method of national music recommendation based on ontology modeling and context awareness to explore the use of music resources to portray user preferences better. First, the expectation-maximization algorithm is used to cluster users and ethnic music scores, and similar users and music are divided into clusters. The similarity of objects in the same cluster is higher, and the similarity of objects in different clusters is lower. Second, we designed a multilayer collaborative filtering ethnic music recommendation model based on ontology modeling and tensor decomposition. This model uses ontology to construct a user knowledge model and integrates similarity measures in multiple situations. The actual case test and user feedback analysis show that the designed personalized national music model has good application and promotion effects.

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“…Although the scale proportion of analog part in digital simulation technology will not exceed 1/4, the error rate of text data bytes from analog part is as high as more than 42% [3]. is problem leads to a significant increase in the proportion of simulated retrieval time and finally directly reduces the retrieval efficiency of the database [4]. Due to the increasingly large data set and increasing internal correlation data of music and audio database, the controllability and predictability of each node data are not high, so it is difficult to judge and determine the wrong database node [5].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

Zheng

2022

Discrete Dynamics in Nature and Society

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In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗ input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database.

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An efficient parallel similarity matrix construction on MapReduce for collaborative filtering

Cited by 19 publications

References 18 publications

Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode

Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode

Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching

[Retracted] Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

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