Modern search engines aggregate results from different verticals : webpages, news, images, video, shopping, knowledge cards, local maps, and so on. Unlike “ten blue links,” these search results are heterogeneous in nature and not even arranged in a list on the page. This revolution directly challenges the conventional “ranked list” formulation in ad hoc search. Therefore, finding proper presentation for a gallery of heterogeneous results is critical for modern search engines. We propose a novel framework that learns the optimal page presentation to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more expressive than a ranked list. It can specify item positions, image sizes, text fonts, and any other styles as long as variations are within business and design constraints. The learned presentation is content aware, i.e., tailored to specific queries and returned results. Simulation experiments show that the framework automatically learns eye-catchy presentations for relevant results. Experiments on real data show that simple instantiations of the framework already outperform leading algorithm in federated search result presentation. It means the framework can learn its own result presentation strategy purely from data, without even knowing the “probability ranking principle.”

中文翻译：

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优化网页搜索的整页展示

现代搜索引擎聚合来自不同的结果垂直行业：网页、新闻、图片、视频、购物、知识卡、本地地图等。与“十个蓝色链接”不同，这些搜索结果本质上是异构的，甚至没有排列在页面上的列表中。这场革命直接挑战了临时搜索中传统的“排名列表”公式。因此，寻找合适的介绍异构结果库对于现代搜索引擎至关重要。我们提出了一个新的框架来学习最优页面展示将异构结果呈现到搜索结果页面 (SERP) 上。页面展示被广泛定义为在 SERP 上展示一组项目的策略，比排名列表更具表现力。它可以指定项目位置、图像大小、文本字体和任何其他样式，只要变化在业务和设计约束范围内。学习的表示是内容感知的，即针对特定查询和返回的结果进行定制。模拟实验表明，该框架自动学习相关结果的引人注目的演示。对真实数据的实验表明，框架的简单实例化在联合搜索结果呈现中已经优于领先算法。这意味着框架可以学习它自己的结果呈现策略，纯粹来自数据，甚至不知道“概率排序原则”。