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Recent research works have shown the robustness towards the recommendation system for athletics using an AI automated system that enhances the longevity of the system. With this model, the automated recommendation helps to improve the quality of athletes during the process of training or other processes. Moreover, domain experts only can understand the rationale of the recommender system where the analyzed data is stored in the cloud system. This research proposes a machine learning–based solution for an athletic dataset that automatically predicts the state of the individual with features like age, gender, calories, temperature, pressure, heart rate, pulse rate, sugar level, respiratory conditions, and state of the body. This research concentrates on modeling a framework for implementing the machine learning approaches with an optimization problem. Here, a novel extreme multi-gradient evolutionary computation (EMGEC) with improved grey wolf optimization (IGWO) is proposed to achieve exploration and exploitation during the selection of features. The dataset collected from the athletes during the marathon (running) is collected from online resources and the feature subsets are extracted from the dataset. The features of these data are analyzed and encoded before placing it over the cloud environment. The performance of the proposed machine learning approach is compared with other approaches and provides better prediction accuracy, precision, recall, and F-measure respectively. The accuracy of the anticipated model is 83.13%, precision is 91.1%, and recall is 91.3% which is substantially higher than of other approaches. The proposed model shows a better trade-off in contrast to prevailing approaches like SVM, RF, k-NN, and logistic regression. [ABSTRACT FROM AUTHOR]

Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. In these work, the attention mechanism is used to select the user interested items in historical behaviors, improving the performance of the CTR predictor. Normally, these attentive modules can be jointly trained with the base predictor by using gradient descents. In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection. For such a problem, we propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper. More specifically, we use a differentiable module as our wrapping operator and then recast its learning problem as a continuous bilevel optimization. Moreover, we use a meta-learning algorithm to solve the optimization and theoretically prove its convergence. Meanwhile, we also provide theoretical analysis to show that our proposed method 1) efficiencies the wrapper-based feature selection, and 2) achieves better resistance to overfitting. Finally, extensive experiments on three public datasets manifest the superiority of our method in boosting the performance of CTR prediction. [ABSTRACT FROM AUTHOR]

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field. [ABSTRACT FROM AUTHOR]