Personalized Item Recommendation Algorithm for Outdoor Sports

Abstract

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.

Sports Recommender Systems: Overview and Research Issues

“…A related approach is presented in Santos-Gago et al [105] who introduce a constraint-based recommendation approach taking into account the current physical situation of athletes and how this can impact physical activities in the context of proposed training plans. To propose different types of outdoor sports activities, Lei et al [75] discuss a collaborative filtering based (combined with a content-based) recommendation approach (to resolve the cold start problem). Case-based recommendation (CBR) can be applied, for example, to infer training practices of persons with a similar running performance.…”