| At the same time as the rapid economic development,people’s life rhythm is accelerating,and the pressure is increasing,and sub-healthy physical condition has become a common phenomenon in society.The fitness industry is rapidly emerging,but existing exercise systems cannot reasonably analyze information and accurately recommend exercise equipment for users.Therefore,this paper aims to improve the recommended suitability of sports equipment and the user’s experience.The main research contents are as follows:(1)Introduce the research background and research significance of recommendation system for sports equipment,analyze the research status of recommendation system and collaborative filtering technology at home and abroad,and finally introduce the main research content and organizational structure of this paper.(2)Introduce the related technologies and some theories used in this paper,mainly including Java and MVC frameworks,and then introduce the related theories used in this paper,mainly including personalized recommendation systems,related recommendation algorithms,and CNN convolution neural networks Deep learning recommendation techniques such as networks,LSTM recurrent neural networks,and BET models.(3)The Attention mechanism is used to extract the user’s historical behavior sequence signal,it can catch sports facilities in the sequence of user behavior between the relevance and importance of the order,will study the depth of decoding network and Attention mechanism,the combination of completely depends on the concentration mechanism of input and output of the global dependency modeling,realizing short-term interests and long-term interests,More effectively capture user interests and recommend candidate exercise equipment.Compared with other methods,it can be seen that although the sports equipment recommendation algorithm based on improved Transformer structure has achieved certain effects in all aspects,it has no outstanding advantages and the operation is not very friendly.Therefore,in order to achieve more obvious results in the recommendation,a new algorithm is introduced.(4)Introduce the main methods used in the current sports equipment recommendation system,and introduce the basic process and problems of collaborative filtering.Aiming at this problem,a hybrid collaborative filtering sports equipment recommendation algorithm based on deep learning is proposed,which can effectively integrate auxiliary information into the potential factors of learning,and improve the performance of the collaborative filtering algorithm through optimization.Finally,in order to prove the effectiveness of the proposed method,the experiment designed in this paper is compared with other methods from all aspects.Experimental results show that the proposed method can effectively improve the recommendation performance of collaborative filtering algorithm and achieve the preset research results.In this paper,a sports equipment reservation and recommendation system based on deep learning is built to improve the efficiency and accuracy of recommendation on the basis of ensuring performance,so that it can serve users more efficiently. |
