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近年来,随着互联网及智能移动设备的发展和普及,丰富了广告的推送方式和投放平台.但是传统的广告推送无法满足用户对个性化广告的需求,导致用户对广告产生抵触情绪,给广告推送带来极大的挑战.个性化广告推荐系统作为应对这些挑战的有效手段,成为个性化服务领域的研究热点之一.个性化广告推荐系统获取用户兴趣偏好,利用多种个性化广告推荐技术,通过PC端、移动终端等多平台为用户提供个性化广告,并且已经在一些应用系统中取得不错的效果.本文对个性化广告推荐系统的研究进展进行系统地综述,从个性化广告推荐的概述出发,对近年来个性化广告推荐的关键技术进行深入分析,包括数据采集与预处理、用户偏好获取、个性化广告推荐技术等.统计分析了个性化广告推荐中使用的多种数据集和评价指标,总结当前个性化广告推荐在传统互联网、移动服务、数字标牌、IPTV等场景下的应用.最后对个性化广告推荐系统存在问题和未来深入研究的方向进行讨论和展望.

http://cjc.ict.ac.cn/online/onlinepaper/zyj-202128100325.pdf

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Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users -- not just the majority group -- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works. However, we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a novel Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we investigate empirically on a new benchmark dataset how effectively the proposed ranking algorithm can control user fairness, item fairness and diversity, as well as the trade-offs between them.

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