"Creativity is the heart and soul of advertising services". Effective
creatives can create a win-win scenario: advertisers can reach target users and
achieve marketing objectives more effectively, users can more quickly find
products of interest, and platforms can generate more advertising revenue. With
the advent of AI-Generated Content, advertisers now can produce vast amounts of
creative content at a minimal cost. The current challenge lies in how
advertising systems can select the most pertinent creative in real-time for
each user personally. Existing methods typically perform serial ranking of ads
or creatives, limiting the creative module in terms of both effectiveness and
efficiency. In this paper, we propose for the first time a novel architecture
for online parallel estimation of ads and creatives ranking, as well as the
corresponding offline joint optimization model. The online architecture enables
sophisticated personalized creative modeling while reducing overall latency.
The offline joint model for CTR estimation allows mutual awareness and
collaborative optimization between ads and creatives. Additionally, we optimize
the offline evaluation metrics for the implicit feedback sorting task involved
in ad creative ranking. We conduct extensive experiments to compare ours with
two state-of-the-art approaches. The results demonstrate the effectiveness of
our approach in both offline evaluations and real-world advertising platforms
online in terms of response time, CTR, and CPM.

广告服务中的创造力是其核心和灵魂。通过 AI 生成的内容，广告商能够以最小的成本创造大量的创意内容。本文首次提出了一种用于在线平行估计广告和创意排名的新架构，以及相应的离线联合优化模型。在线架构能够进行个性化的创意建模，同时降低总体延迟。CTR 估计的离线联合模型允许广告和创意之间进行相互感知和协同优化。我们对比了两种最先进的方法进行了广泛的实验。结果表明，我们的方法在离线评估和真实世界的在线广告平台中都具有响应时间、CTR 和 CPM 方面的有效性。

实时广告系统中广告和创意的并行排名

Parallel Ranking of Ads and Creatives in Real-Time Advertising Systems

The current wave of deep learning (the hyper-vitamined return of artificial
neural networks) applies not only to traditional statistical machine learning
tasks: prediction and classification (e.g., for weather prediction and pattern
recognition), but has already conquered other areas, such as translation. A
growing area of application is the generation of creative content, notably the
case of music, the topic of this paper. The motivation is in using the capacity
of modern deep learning techniques to automatically learn musical styles from
arbitrary musical corpora and then to generate musical samples from the
estimated distribution, with some degree of control over the generation. This
paper provides a tutorial on music generation based on deep learning
techniques. After a short introduction to the topic illustrated by a recent
exemple, the paper analyzes some early works from the late 1980s using
artificial neural networks for music generation and how their pioneering
contributions have prefigured current techniques. Then, we introduce some
conceptual framework to analyze the various concepts and dimensions involved.
Various examples of recent systems are introduced and analyzed to illustrate
the variety of concerns and of techniques.

本文介绍了基于深度学习技术的音乐生成方法，包括早期使用人工神经网络的作品和最近的系统，使用现代深度学习技术和控制手段生成具有不同音乐风格的音乐。

从人工神经网络到深度学习在音乐生成中的历史、概念和趋势

From Artificial Neural Networks to Deep Learning for Music Generation --  History, Concepts and Trends

The notion of creativity, as opposed to related concepts such as beauty or
interestingness, has not been studied from the perspective of automatic
analysis of multimedia content. Meanwhile, short online videos shared on social
media platforms, or micro-videos, have arisen as a new medium for creative
expression. In this paper we study creative micro-videos in an effort to
understand the features that make a video creative, and to address the problem
of automatic detection of creative content. Defining creative videos as those
that are novel and have aesthetic value, we conduct a crowdsourcing experiment
to create a dataset of over 3,800 micro-videos labelled as creative and
non-creative. We propose a set of computational features that we map to the
components of our definition of creativity, and conduct an analysis to
determine which of these features correlate most with creative video. Finally,
we evaluate a supervised approach to automatically detect creative video, with
promising results, showing that it is necessary to model both aesthetic value
and novelty to achieve optimal classification accuracy.

研究了微视频的创造性，旨在尝试理解构成一个有创造性的视频的特征以及解决自动检测有创造性内容的问题。通过提出一组计算特征，并且进行了分析来确定这些特征与创意视频之间的相关性，并通过监督方法进行自动检测的评估，最终取得了不错的结果，表明了成果的创造性与美学价值和新颖性模型化的必要性。