Abstract
In order to improve the effectiveness of national fitness programs, this article analyzes the method of national fitness items recommendation based on a neural network algorithm. By using the time and space characteristics of fitness users’ sign-in, a novel POI recommendation model is proposed, and a novel fusion method is proposed to combine similarity and spatial similarity to achieve the final similarity calculation based on fitness users’ temporal and spatial preferences. In addition, in order to model the spatial similarity of fitness users, the Voronoi diagram is constructed by using the geographic locations of all POIs. Finally, this paper constructs a recommendation system for national fitness items based on a neural network algorithm. The experimental research results show that the national fitness program recommendation system proposed in this article basically meets the expected demand.
Highlights
Personal information query Physical fitness test evaluation query Exercise prescription query store the top k most similar fitness users of the target fitness user Ui. erefore, |Sui| k, a recommended list of target fitness user ui. e detailed steps are as follows: (1) e algorithm constructs a set CPui, which contains all the POIs visited by fitness users in Sui
National Fitness Item Recommendation System Based on a Neural Network Algorithm
The problem that China faces in national fitness is the uneven development of public services. e main task at this stage is to use the construction of public service facilities to solve the problems and contradictions in China’s fitness industry
Summary
For the understanding of the national fitness program’s mechanism, Ferguson et al.’s [4] point of view is that it can provide all the people with basic conditions for physical exercise activities and the creation of the environment to meet public’s basic requirements for physical fitness. e physical quality has been significantly improved, and the related services and guarantee systems are produced by it. Pulido et al [15] points out that the establishment of a national fitness service mechanism needs to focus on research and development from the following five aspects: publicity, construction of facilities and equipment, scientific guidance, dynamic monitoring, and organizational management. In order to model the time characteristics of fitness users, we construct a third-order tensor X ∈ RU×T×C, where U, T, and C represent the number of fitness users, the number of time intervals, and the number of categories, respectively. E tensor decomposition can be used to alleviate missing or sparse data and explore the potential associations of fitness users, time, and POI categories To avoid those negative values which have no meaning for the preference measure during the restoration process, we add non-negative constraints in the decomposition process. PTi(1, 1) represents the access probability of the fitness user ui accessing the POI of the category c1 at a time interval t1. erefore, the similarity of preferences of different fitness users based on time is expressed as follows [16]:
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