The transfer fees of sports players have become astronomical. This is because
bringing players of great future value to the club is essential for their
survival. We present a case study on the key factors affecting the world's top
soccer players' transfer fees based on the FIFA data analysis. To predict each
player's market value, we propose an improved LightGBM model by optimizing its
hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm. We
identify prominent features by the SHapley Additive exPlanations (SHAP)
algorithm. The proposed method has been compared against the baseline
regression models (e.g., linear regression, lasso, elastic net, kernel ridge
regression) and gradient boosting model without hyperparameter optimization.
The optimized LightGBM model showed an excellent accuracy of approximately 3.8,
1.4, and 1.8 times on average compared to the regression baseline models, GBDT,
and LightGBM model in terms of RMSE. Our model offers interpretability in
deciding what attributes football clubs should consider in recruiting players
in the future.

通过 FIFA 数据分析，本文针对足球运动员转会费用的主要影响因素，提出了基于 LightGBM 模型的改进方法，采用树形 Parzen 算法进行超参优化，并通过 SHapley Additive exPlanations 算法进行特征筛选。与基线回归模型和梯度提升模型相比，优化后的 LightGBM 模型以 RMSE 为标准的表现约为回归基线模型、GBDT 和 LightGBM 模型的 3.8 倍、1.4 倍和 1.8 倍，具有很好的准确性。该模型提供了运动俱乐部在未来招募球员时所需考虑的属性解释。

贝叶斯集成方法预测足球运动员价值

Prediction of Football Player Value using Bayesian Ensemble Approach

Real estate appraisal refers to the process of developing an unbiased opinion
for real property's market value, which plays a vital role in decision-making
for various players in the marketplace (e.g., real estate agents, appraisers,
lenders, and buyers). However, it is a nontrivial task for accurate real estate
appraisal because of three major challenges: (1) The complicated influencing
factors for property value; (2) The asynchronously spatiotemporal dependencies
among real estate transactions; (3) The diversified correlations between
residential communities. To this end, we propose a Multi-Task Hierarchical
Graph Representation Learning (MugRep) framework for accurate real estate
appraisal. Specifically, by acquiring and integrating multi-source urban data,
we first construct a rich feature set to comprehensively profile the real
estate from multiple perspectives (e.g., geographical distribution, human
mobility distribution, and resident demographics distribution). Then, an
evolving real estate transaction graph and a corresponding event graph
convolution module are proposed to incorporate asynchronously spatiotemporal
dependencies among real estate transactions. Moreover, to further incorporate
valuable knowledge from the view of residential communities, we devise a
hierarchical heterogeneous community graph convolution module to capture
diversified correlations between residential communities. Finally, an urban
district partitioned multi-task learning module is introduced to generate
differently distributed value opinions for real estate. Extensive experiments
on two real-world datasets demonstrate the effectiveness of MugRep and its
components and features.

该研究提出了一种基于多任务层次图表示学习（MugRep）的房地产精确估价框架，通过获取和集成多源城市数据，构建一个丰富的特征集，包括地理分布、人类流动分布和居民人口统计分布，并提出了一种异步时空依赖性的房地产交易图和相关事件图卷积模块，以及一种分层异质社区图卷积模块，以捕捉住宅社区之间的多样化关系，并引入了一种城市区域划分的多任务学习模块，用于为房地产生成具有不同分布的价值意见。

MugRep：面向房地产估价的多任务分层图表示学习框架

MugRep: A Multi-Task Hierarchical Graph Representation Learning  Framework for Real Estate Appraisal

Real-time bidding (RTB) based display advertising has become one of the key
technological advances in computational advertising. RTB enables advertisers to
buy individual ad impressions via an auction in real-time and facilitates the
evaluation and the bidding of individual impressions across multiple
advertisers. In RTB, the advertisers face three main challenges when optimizing
their bidding strategies, namely (i) estimating the utility (e.g., conversions,
clicks) of the ad impression, (ii) forecasting the market value (thus the cost)
of the given ad impression, and (iii) deciding the optimal bid for the given
auction based on the first two. Previous solutions assume the first two are
solved before addressing the bid optimization problem. However, these
challenges are strongly correlated and dealing with any individual problem
independently may not be globally optimal. In this paper, we propose Bidding
Machine, a comprehensive learning to bid framework, which consists of three
optimizers dealing with each challenge above, and as a whole, jointly optimizes
these three parts. We show that such a joint optimization would largely
increase the campaign effectiveness and the profit. From the learning
perspective, we show that the bidding machine can be updated smoothly with both
offline periodical batch or online sequential training schemes. Our extensive
offline empirical study and online A/B testing verify the high effectiveness of
the proposed bidding machine.

本论文提出了一个综合的学习出价框架 Bidding Machine，能够联合优化估算广告效用、预测市场价值、制定最佳竞标策略三个挑战，大大提高了广告活动的效益和利润。