In search advertising, the search engine needs to select the most profitable advertisements to display, which can be formulated as an instance of online learning with partial feedback, also known as the stochastic multi-armed bandit (MAB) problem. In this paper, we show that the naive application of MAB algorithms to search advertising for advertisement selection will produce sample selection bias that harms the search engine by decreasing expected revenue and "estimation of the largest mean" (ELM) bias that harms the advertisers by increasing game-theoretic player-regret. We then propose simple bias-correction methods with benefits to both the search engine and the advertisers.

Current keyword decision-making in sponsored search advertising relies on large, static datasets, limiting the ability to automatically set up keywords and adapt to real-time KPI metrics and product updates that are essential for effective advertising. In this paper, we propose On-the-fly Keyword Generation (OKG), an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real time, aligning with strategies recommended by advertising platforms. Additionally, we introduce the first publicly accessible dataset containing real keyword data along with its KPIs across diverse domains, providing a valuable resource for future research. Experimental results show that OKG significantly improves keyword adaptability and responsiveness compared to traditional methods. The code for OKG and the dataset are available at https://github.com/sony/okg.

In search advertising, the search engine needs to select the most profitable advertisements to display, which can be formulated as an instance of online learning with partial feedback, also known as the stochastic multi-armed bandit (MAB) problem. In this paper, we show that the naive application of MAB algorithms to search advertising for advertisement selection will produce sample selection bias that harms the search engine by decreasing expected revenue and "estimation of the largest mean" (ELM) bias that harms the advertisers by increasing game-theoretic player-regret. We then propose simple bias-correction methods with benefits to both the search engine and the advertisers.

When Google was officially launched in 1998 by Larry Page and Sergey Brin, it was the 21st search engine to enter the market. In 2022 Google generated over $200 billion in revenue off of search advertising and other digital advertising. MIcrosoft launched the Bing search engine in 2009, built from the assets of Live Search which was released in 2006. By all accounts, Microsoft Bing was the laggard among Google and Yahoo in the space. Round 1 of the search engine wars was won by Google which has dominate for almost two decades.

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.

ReverseAds announced the launch of its reverse-engineered search advertising solution that uses Big Data, A.I., and predictive modeling to help brands serve intuitive ads everywhere buyers go online after their initial search. ReverseAds addresses the need for predictive multi-channel ad campaigns that provide total visibility of the buyer's journey, allowing brands to move beyond underperforming search ads. This approach to digital advertising prioritizes ROI and CPA compared to the CPC bidding model provided by Google. With ReverseAds, considered purchase brands gain access to unprecedented amounts of intent data and a USPTO provisional patent-approved Assignment Algorithm. The algorithm uses predictive learning A.I. to determine which keywords will drive a business's highest total conversion.

In search advertising, the search engine needs to select the most profitable advertisements to display, which can be formulated as an instance of online learning with partial feedback, also known as the stochastic multi-armed bandit (MAB) problem. In this paper, we show that the naive application of MAB algorithms to search advertising for advertisement selection will produce sample selection bias that harms the search engine by decreasing expected revenue and "estimation of the largest mean" (ELM) bias that harms the advertisers by increasing game-theoretic player-regret. We then propose simple bias-correction methods with benefits to both the search engine and the advertisers. Papers published at the Neural Information Processing Systems Conference.

Whenever you search on Google for something you're thinking about buying, you're using a selection of keywords and signalling your intent to make a purchase. If you search for "brown shoes" and adverts appear in your search results suggesting where to get brown shoes, you've experienced "search retargeting", a technique that brands are combining with artificial intelligence (AI) to figure out what makes consumers tick. By mapping and analysing the keywords that consumers use in their search queries, brands can learn more about what people actually want to buy. "Keywords are a great signal of intent," says Carl White, co-founder of Nano Interactive, which provides search targeting technology to brands. "With the developments of AI techniques, you can feel out what people are really searching for. And you can make strong assumptions about what their intent is based on the kind of combination of keywords."

Search advertising is the placement of ads in search engine results. Businesses pay to place these ads at the top of search results. The price paid depends on several factors, such as search term popularity, competition and website quality. Taking a scroll through Google AdWords' lessons and picking up a certificate is a great way to start yourself off with paid search. But it doesn't give you everything you need to know.

Artificial intelligence, or AI, has been a top buzzword over the last year. In fact, AI was even named the "Marketing Word of the Year" in 2017 by the Association of National Advertisers (ANA). There are so many opinions and prognostications about the future of AI – with industry leaders and others weighing in – that it can be difficult to understand. Facebook's Mark Zuckerberg recently shared his optimism over the rise of AI technologies like deep learning and how they could lead to breakthroughs in areas like healthcare and self-driving cars. Space X founder Elon Musk contends that "AI is more dangerous than nuclear weapons."