Display Ads and the generalized assignment problem are two well-studied online packing problems with important applications in ad allocation and other areas.

Display Ads and the generalized assignment problem are two well-studied online packing problems with important applications in ad allocation and other areas.

Display Ads and the generalized assignment problem are two well-studied online packing problems with important applications in ad allocation and other areas. In both problems, ad impressions arrive online and have to be allocated immediately to budget-constrained advertisers. Worst-case algorithms that achieve the ideal competitive ratio are known, but might act overly conservative given the predictable and usually tame nature of real-world input. Given this discrepancy, we develop an algorithm for both problems that incorporate machine-learned predictions and can thus improve the performance beyond the worst-case. Our algorithm is based on the work of Feldman et al. (2009) and similar in nature to Mahdian et al. (2007) who were the first to develop a learning-augmented algorithm for the related, but more structured Ad Words problem. We use a novel analysis to show that our algorithm is able to capitalize on a good prediction, while being robust against poor predictions. We experimentally evaluate our algorithm on synthetic and real-world data on a wide range of predictions. Our algorithm is consistently outperforming the worst-case algorithm without predictions.

Google's ad business will celebrate its 23rd year this fall, but not before paid search undergoes massive changes. It's unlikely Google will sit idly by and take massive revenue hits as paid placements decline. We don't yet know what they will roll out to capitalize on AI-powered search – or when exactly users will see ads in this AI-powered experience. We do know there will be fewer advertising opportunities, increased competition and higher costs. But AI will also help advertisers better target consumers – and it could help them optimize campaigns, too.

As an online or physical retail company, your goal is to sell as many products as possible to the public. While traditional advertising methods would have you putting leaflets through doors, the modern marketing approach is all about online ads. Currently, Google Ads is one of the biggest online advertising platforms. However, Google isn't the easiest advertising platform to understand and implement. Especially for those new to the marketing niche, you might be confused by the many different options.

Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, which is a time-consuming approach that often requires domain expertise. We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks that can predict user attention to direct displays, such as SERP advertisements. Our models are trained over raw mouse cursor data and achieve competitive performance. We conclude that neural network models should be adopted for downstream tasks involving mouse cursor movements, since they can provide an invaluable implicit feedback signal for re-ranking and evaluation.

Traditional display ads have been the whipping boy of the digital advertising industry for some time now, given their low engagement rates and proclivity for accidental clicks. These days more than ever, static and even basic rich media banner ads represent the antithesis of what consumers want from their online experiences. And in whisking people away to third-party sites in the rare event of a click, they lack a pleasant user experience. Meanwhile, even as the effectiveness of these units declines, marketplaces like Google Display Network have become expensive and largely saturated across industries. Yet brands continue to invest money into display because they know no other way to reach audiences effectively and at scale.

Some marketers eager for a new digital ad alternative are also conflicted about the rise of Amazon--a competing retailer with its own in-house brands to sell--setting up a new potential source of tension. Amazon's ad revenue is on pace to double this year, to $5.83 billion, according to eMarketer. Its ad sales are expected to jump $28.4 billion over the next five years, according to Cowen & Co.--more than the combined increases in ad revenue for all television networks globally, according to figures from media-buyer GroupM. The cumulative effect is an earthquake whose tremors will be felt by anyone selling ads, including digital publishers and TV networks. Retailers like Walmart Inc., Target Corp. and Kroger Co., which get paid by brands to place products in desirable locations within their stores, are already losing business to Amazon, ad executives say.

Emirates airline is using artificial intelligence for a chatbot that lies within display ads for its Emirates Vacations unit. The ads allow people to ask travel and trip questions and receive answers immediately within the ad unit. But the company believes AI can be impactful for another application: breaking down additional friction points when it comes to search. The chatbot will recommend destinations and vacation packages based on the context of users' questions, the content on the site it appears on and Emirates Vacations' inventory. For instance, if Emirates Vacations doesn't have a hotel in Toronto, the chatbot won't suggest a hotel in Toronto.

If you got wedged under a rock in 2017, it may be both a blessing and a curse that you missed the CRM AI media frenzy. AI showed up everywhere, rivaling electricity's systemic emergence a century ago, allegedly injecting sage-like wisdom into everything from sales forecasting tools to email subject lines generators. But buildup and hype aside, real progress was made in some impactful areas as unprecedented investments poured in. More resources supporting great minds pushed forward innovation in areas like image recognition, voice technologies, and natural language generation (NLG). And savvy brands that mindfully wired these into CRM applications boosted performance, in some cases realizing 400 percent ROI.