Most people primarily rely on subjective opinions and action assessment from others in the process of learning badminton, which leads to biases and unreliable assessment of player performance. This paper presents a method of deep learning-based recognition and quality assessment of badminton actions to accurately identify player actions and assess player performance. In this research, we construct a video dataset of standard badminton actions for training networks and design a human pose estimation and tracking network to detect keypoints of individual players in the video dataset and track their trajectories. Furthermore, badminton action recognition is carried out based on the SlowFast network framework, and a Siamese network with it as the backbone network is proposed for automating the quality assessment of badminton actions. Experimental results demonstrate that the mean average precision (mAP) of human body pose estimation reaches 83.2%, the Multiple Object Tracking Accuracy (MOTA) of pose detection and recognition in badminton games reaches 81.4%, and the Multiple Object Tracking Precision (MOTP) reaches 90.7%. The accuracy of professional players in identifying badminton strokes is 83.08% for Top-1 and 96.89% for Top-3. Therefore, the proposed method can be effectively applied to badminton action recognition and quality assessment.

The quality of features plays an important role in the performance of recommender systems. Recognizing this, feature selection has emerged as a crucial technique in refining recommender systems. Recent advancements leveraging Automated Machine Learning (AutoML) has drawn significant attention, particularly in two main categories: early feature selection and late feature selection, differentiated by whether the selection occurs before or after the embedding layer. The early feature selection selects a fixed subset of features and retrains the model, while the late feature selection, known as adaptive feature selection, dynamically adjusts feature choices for each data instance, recognizing the variability in feature significance. Although adaptive feature selection has shown remarkable improvements in performance, its main drawback lies in its post-embedding layer feature selection. This process often becomes cumbersome and inefficient in large-scale recommender systems with billions of ID-type features, leading to a highly sparse and parameter-heavy embedding layer. To overcome this, we introduce Adaptive Early Feature Selection (AEFS), a very simple method that not only adaptively selects informative features for each instance, but also significantly reduces the activated parameters of the embedding layer. AEFS employs a dual-model architecture, encompassing an auxiliary model dedicated to feature selection and a main model responsible for prediction. To ensure effective alignment between these two models, we incorporate two collaborative training loss constraints. Our extensive experiments on three benchmark datasets validate the efficiency and effectiveness of our approach. Notably, AEFS matches the performance of current state-of-theart Adaptive Late Feature Selection methods while achieving a significant reduction of 37. 5% in the activated parameters of the embedding layer. We believe that this work opens up new possibilities for feature selection.