Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean – content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context.

CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party Web pages, but the ads in this form are rich multimedia ads – image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising.

Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query/content and the ad. Also, as the user’s probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser’s side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above.

Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end.

计算广告，通常称为在线广告或Web广告，是指找到与Web上特定上下文匹配的最相关的广告。上下文取决于广告的类型，并且可能意味着–显示广告的内容，正在查看广告的用户或用户的社交网络。计算广告（CA）是位于信息检索，统计建模，机器学习，优化，大规模搜索和文本分析相交处的科学子学科。计算广告中解决的核心问题是广告与上下文之间的匹配。

CA以三种主要形式在Web上盛行。其中一种形式涉及在搜索页面上显示与查询相关的文字广告，这称为赞助商搜索。另一方面，显示与第三方网页内容相关的文字广告被称为内容相关广告。广告的第三种形式还涉及在第三方网页上放置广告，但是这种形式的广告是丰富的多媒体广告-图片，视频，音频，Flash。带有富媒体广告的商业模式与带有文本广告的商业模式略有不同。这些广告也称为横幅广告，这种广告形式称为展示广告。

赞助搜索和上下文广告都涉及针对不同类型的内容（查询和网页）检索相关广告。由于广告简短且主要是为了吸引用户而写的，因此广告的检索带来了挑战，例如查询/内容与广告之间的词汇不匹配。而且，随着用户检查广告的可能性随着广告在排名列表中的位置而降低，必须将最佳广告保持在最高位置。展示广告带来了一些挑战，包括对用户行为和嘈杂的页面内容进行建模以及在广告商方面优化出价。此外，在线广告还面临着虚假出价，点击垃圾邮件和广告垃圾邮件等挑战。这些挑战在所有形式的广告中普遍存在。在过去的十年半中，在CA的不同领域发表了大量的研究工作。本次调查的重点是讨论与CA的信息检索，机器学习和统计领域有关的问题和解决方案。该调查涵盖了解决上述几个问题的技术和方法。

计算广告的研究随着时间的推移而发展，目前在传统领域（词汇失配，查询重写，点击预测）和最近确定的领域（用户定位，移动广告，社交广告）中都在继续。在这项研究中，我们主要关注传统领域中提出的问题和解决方案，并在调查的后半部分简要介绍新兴领域。为了促进将来的研究，最后还提供了对可用资源，公共基准数据集列表和未来工作方向的讨论。