Department of Physical Education, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China.
Department of Physical Education, Huazhong Agricultural University, Wuhan 430070, Hubei, China.
Comput Intell Neurosci. 2022 Jul 31;2022:8282257. doi: 10.1155/2022/8282257. eCollection 2022.
With the rapid development of China's economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of "information overload," and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it.
随着中国经济的快速发展，人们渴望找到一种既能缓解工作压力又能增强健康的有效方法。户外运动是人们的最佳选择之一。然而，网络上推荐的数据量非常大。因此，当人们通过网络了解户外运动时，他们无法有效地获取他们想要的信息。这就是“信息过载”的问题，个性化推荐系统可以有效地缓解这个问题。为了有效地向用户推荐户外运动，本文在户外运动个性化推荐系统中进行了有益的尝试。本文的具体工作如下：首先，总结了中国户外运动的现状，并研究了推荐系统的相关技术，包括用户建模技术、推荐目标建模技术和推荐算法。为了获得更好的推荐效果，本文提出混合使用基于用户的协同过滤推荐算法、基于项目的协同过滤推荐算法和基于内容的推荐算法。混合算法采用特征扩展和加权组合的方式。首先，得到基于用户的协同过滤推荐和基于内容的推荐的混合模型（模型 1）。其次，得到基于项目协同过滤推荐和基于内容的推荐的混合模型（模型 2）。最后，将模型 1 和模型 2 结合起来，得到最终推荐效果更好的混合模型。对于推荐系统中的常见冷启动问题，系统采用基于内容的推荐算法来解决。
