Contextual advertising

Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor. © 2012 INFORMS

CONTEXTUAL ADVERTISING: MATCHING CONTENTS WITH POTENTIAL VIEWERS

Advertisement is the form of information use by the company to deliver marketing messages to attract potential customers. Matching the right content in the right place is important in managing online advertising. The matching content tool is used in the advertising company to insure the online advertisement that display in the viewer screen are matched to the information that searched. Viewers will feel comfortable with the right advertise content that really compliment their needs. The advertisement attracts the viewer to buy the product and service that advertiser offer. This made advertiser achieved the main goal of the investment in the advertising. This project purpose is to improve or enhance the management of the advertisement that match the viewer need and searching through the internet platform. The conventional online advertisements are display according to the bidding rate involving publisher and advertiser agreement. The project also tries to focus on optimizing revenue from matched advertising tool use in investing for online advertisement

Contextual Advertising Based on Content Recognition in a Video

Generally, the present disclosure is directed to providing relevant advertisements based on the visual content of a video. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to determine a relevant advertisement and/or a relevant time for the relevant advertisement based on image data taken from a video

Sequential Selection of Correlated Ads by POMDPs

Online advertising has become a key source of revenue for both web search engines and online publishers. For them, the ability of allocating right ads to right webpages is critical because any mismatched ads would not only harm web users' satisfactions but also lower the ad income. In this paper, we study how online publishers could optimally select ads to maximize their ad incomes over time. The conventional offline, content-based matching between webpages and ads is a fine start but cannot solve the problem completely because good matching does not necessarily lead to good payoff. Moreover, with the limited display impressions, we need to balance the need of selecting ads to learn true ad payoffs (exploration) with that of allocating ads to generate high immediate payoffs based on the current belief (exploitation). In this paper, we address the problem by employing Partially observable Markov decision processes (POMDPs) and discuss how to utilize the correlation of ads to improve the efficiency of the exploration and increase ad incomes in a long run. Our mathematical derivation shows that the belief states of correlated ads can be naturally updated using a formula similar to collaborative filtering. To test our model, a real world ad dataset from a major search engine is collected and categorized. Experimenting over the data, we provide an analyse of the effect of the underlying parameters, and demonstrate that our algorithms significantly outperform other strong baselines