Predicting Market Value in Professional Soccer: Insights from Explainable Machine Learning Models

This study presents an innovative method for predicting the market value of professional soccer players using explainable machine learning models. Using a dataset curated from the FIFA website, we employ an ensemble machine learning approach coupled with Shapley Additive exPlanations (SHAP) to provide detailed explanations of the models' predictions. The GBDT model achieves the highest mean R-Squared (0.8780) and the lowest mean Root Mean Squared Error (3,221,632.175), indicating its superior performance among the evaluated models. Our analysis reveals that specific skills such as ball control, short passing, finishing, interceptions, dribbling, and tackling are paramount within the skill dimension, whereas sprint speed and acceleration are critical in the fitness dimension, and reactions are preeminent in the cognitive dimension. Our results offer a more accurate, objective, and consistent framework for market value estimation, presenting useful insights for managerial decisions in player transfers.

Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation

Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable Data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL.

Reweighting Clicks with Dwell Time in Recommendation

The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title-content mismatching, and thus fail to precisely capture users' real satisfaction on items. Dwell time could be viewed as a high-quality quantitative indicator of user preferences on each click, while existing recommendation models do not fully explore the modeling of dwell time. In this work, we focus on reweighting clicks with dwell time in recommendation. Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time. Next, we propose a normalized dwell time function to reweight click signals in training, which could better guide our model to provide a high-quality and efficient reading. The Click reweighting model achieves significant improvements on both offline and online evaluations in a real-world system.