Sports recommender systems receive an increasing attention due to their
potential of fostering healthy living, improving personal well-being, and
increasing performances in sport. These systems support people in sports, for
example, by the recommendation of healthy and performance boosting food items,
the recommendation of training practices, talent and team recommendation, and
the recommendation of specific tactics in competitions. With applications in
the virtual world, for example, the recommendation of maps or opponents in
e-sports, these systems already transcend conventional sports scenarios where
physical presence is needed. On the basis of different working examples, we
present an overview of sports recommender systems applications and techniques.
Overall, we analyze the related state-of-the-art and discuss open research
issues.

体育推荐系统已引起了越来越多的关注，因为它们潜在的促进健康生活、提高个人福祉和提升运动表现的能力。本文通过不同的工作实例，对体育推荐系统的应用和技术进行了概述，并分析了相关的最新技术并讨论了开放的研究问题。

体育推荐系统：概述与研究问题

Sports Recommender Systems: Overview and Research Issues

The imbalanced distribution of long-tailed data poses a challenge for deep
neural networks, as models tend to prioritize correctly classifying head
classes over others so that perform poorly on tail classes. The lack of
semantics for tail classes is one of the key factors contributing to their low
recognition accuracy. To rectify this issue, we propose to augment tail classes
by borrowing the diverse semantic information from head classes, referred to as
head-to-tail fusion (H2T). We randomly replace a portion of the feature maps of
the tail class with those of the head class. The fused feature map can
effectively enhance the diversity of tail classes by incorporating features
from head classes that are relevant to them. The proposed method is easy to
implement due to its additive fusion module, making it highly compatible with
existing long-tail recognition methods for further performance boosting.
Extensive experiments on various long-tailed benchmarks demonstrate the
effectiveness of the proposed H2T. The source code is temporarily available at
this https URL

本文中提出了一种用于解决长尾数据中深度神经网络的头部类别与尾部类别不平衡分布所导致的性能问题的解决方案 —— 头尾融合（H2T），并且通过各种长尾数据基准测试表明了该方案的有效性。

从头到尾的特征融合：一种极端的长尾视觉识别增强策略

Feature Fusion from Head to Tail: an Extreme Augmenting Strategy for  Long-Tailed Visual Recognition

Person search by natural language aims at retrieving a specific person in a
large-scale image pool that matches the given textual descriptions. While most
of the current methods treat the task as a holistic visual and textual feature
matching one, we approach it from an attribute-aligning perspective that allows
grounding specific attribute phrases to the corresponding visual regions. We
achieve success as well as the performance boosting by a robust feature
learning that the referred identity can be accurately bundled by multiple
attribute visual cues. To be concrete, our Visual-Textual Attribute Alignment
model (dubbed as ViTAA) learns to disentangle the feature space of a person
into subspaces corresponding to attributes using a light auxiliary attribute
segmentation computing branch. It then aligns these visual features with the
textual attributes parsed from the sentences by using a novel contrastive
learning loss. Upon that, we validate our ViTAA framework through extensive
experiments on tasks of person search by natural language and by
attribute-phrase queries, on which our system achieves state-of-the-art
performances. Code will be publicly available upon publication.

本文提出一种基于属性对齐的视觉 - 文本对齐方法 (ViTAA)，通过较轻的属性分割计算模块分离人物特征空间并使用对比学习损失函数对视觉和文本属性进行对齐，从而成功提升了自然语言下的人物搜索和属性 - 短语查询任务的表现，实现了最先进的结果。