<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>It is helpful for athletes to obtain real-time and accurate sports information resources by integrating various recommendation algorithms into a personalised sports recommendation system. Therefore, this paper designs user-based and content-based recommendation algorithms, and recommends their results to sporters by weighted selection. In addition, by acquiring the characteristics of sporters, the method of the constraint space of core parameters is determined by using ontology rule reasoning, which ensures the rationality of sports parameter extraction, and thus establishes the similarity calculation model of sports resources and designs the strategy and method of model verification. Finally, the validity of the model is evaluated by Mahalanobis distance, and the recommendation effects of different algorithms are compared by similarity grade, which can provide sporters with personalised sports resources with stronger pertinence and better effect.</p>
</abstract>

It is helpful for athletes to obtain real-time and accurate sports information resources by integrating various recommendation algorithms into a personalised sports recommendation system. Therefore, this paper designs user-based and content-based recommendation algorithms, and recommends their results to sporters by weighted selection. In addition, by acquiring the characteristics of sporters, the method of the constraint space of core parameters is determined by using ontology rule reasoning, which ensures the rationality of sports parameter extraction, and thus establishes the similarity calculation model of sports resources and designs the strategy and method of model verification. Finally, the validity of the model is evaluated by Mahalanobis distance, and the recommendation effects of different algorithms are compared by similarity grade, which can provide sporters with personalised sports resources with stronger pertinence and better effect.

Calculation and evaluation of similarity in sports information resources based on combined algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

It is helpful for athletes to obtain real-time and accurate sports information resources by integrating various recommendation algorithms into a personalised...

Calculation and evaluation of similarity in sports information resources based on combined algorithm

Online veröffentlicht: 31. Juli 2023

Seitenbereich: -

Eingereicht: 12. Apr. 2022

Akzeptiert: 22. Aug. 2022

DOI: https://doi.org/10.2478/amns.2022.2.00201

Schlüsselwörter
Recommendation algorithm, sports information similarity model, core parameters

© 2024 Shuang Zhou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Calculation and evaluation of similarity in sports information resources based on combined algorithm

Online veröffentlicht: 31. Juli 2023

Seitenbereich: -

Eingereicht: 12. Apr. 2022

Akzeptiert: 22. Aug. 2022

DOI: https://doi.org/10.2478/amns.2022.2.00201

SchlüsselwörterRecommendation algorithm, sports information similarity model, core parameters

© 2024 Shuang Zhou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Recommendation algorithm, sports information similarity model, core parameters