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Digitalization and Information Management Mechanism of Sports Events Based on Multisensor Node Cooperative Perception Model
Author(s) -
Yi Liu,
Yaodong Wang,
Yuntong Tan,
Jie Ma,
Yan Zhuang,
Xiangqian Zhao
Publication year - 2022
Publication title -
journal of sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.399
H-Index - 43
eISSN - 1687-7268
pISSN - 1687-725X
DOI - 10.1155/2022/6430191
Subject(s) - event (particle physics) , computer science , service (business) , set (abstract data type) , information retrieval , process (computing) , node (physics) , service provider , data mining , world wide web , engineering , physics , economy , structural engineering , quantum mechanics , economics , programming language , operating system
In the process of developing major sports events, how to guide providers and users to provide and utilize the archives information resources of major sports events and realize the interaction between them is an important problem to be solved urgently in the development of major sports events and the archive service of major sports events. By analyzing the present situation of archive service of major sports events, especially the analysis of the opposite dependent subjects of service providers and users, we can see that the continuous development of archive services for major sports events will inevitably lead to constant changes in user groups and user needs, guided by the theory of information retrieval, knowledge management, and media effect. According to the service model of archive service of major sports events, the archive service model of specific sports events is constructed. In this paper, four kinds of event recommendation models are applied to the collected marathon event data for experiments. Through experimental comparison, the effectiveness of content-based recommendation algorithm technology in the event network data set is verified, and an algorithm model suitable for marathon event recommendation is obtained. Experiments show that the comprehensive event recommendation model based on term frequency–inverse document frequency (TF-IDF) text weight and Race2vec entry sequence has the best recommendation performance on marathon event data set. According to the recommendation target of the event and the characteristics of the event data type, we can choose a single or comprehensive recommendation algorithm to build a model to realize the event recommendation.

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