First we prove the important result stated last lecture about sponsored search auctions. Recall the setup: there are k slots with click-through-rates (CTRs) α1 ≥ α2 ≥ · · · ≥ αk, and each bidder i = 1, 2, . . . , n has a private valuation per-click vi. (Bids and prices are also per-click.) Our first attempt to generalize the Vickrey auction to sponsored search results in the “Generalized Second...

Clicking on a virtual object is the most fundamental and important interaction in augmented reality (AR). However, existing AR systems do not support natural click interfaces, because headmounted displays with only one camera are usually adopted to realize augmented reality and it is difficult to recognize an arbitrary gesture without accurate depth information. For the ease of detection, some ...

Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user’s behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what quer...

This paper presents models for predicted click-through rates in position auctions that take into account two possibilities that are not normally considered—that the identities of ads shown in other positions may affect the probability that an ad in a particular position receives a click (externalities) and that some ads may be less adversely affected by being shown in a lower position than othe...

Sponsored search accounts for 40% of the total online advertising market. These ads appear as ordered lists along with the regular search results in search engine results pages. The conventional wisdom in the industry is that the top position is the most desirable position for advertisers. This has led to intense competition among advertisers to secure the top positions in the results pages. We...

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In sponsored search it is critical to match ads that are relevant to a query and to accurately predict their likelihood of being clicked. Commercial search engines typically use machine learning models for both query-ad relevance matching and click-through-rate (CTR) prediction. However, matching models are based on the similarity between a query and an ad, ignoring the fact that a retrieved ad...

The challenge for Track 2 of the KDD Cup 2012 competition was to predict the click-through rate (CTR) of web advertisements given information about the ad, the query and the user. Our solution comprised an ensemble of models, combined using an artificial neural network. We built collaborative filters, probability models, and feature engineered models to predict CTRs. In addition, we developed a...

In the beginning, search engines provide placements next to the original search results for advertisers on specific keywords. Since users often search for their interests or purchasing decision, timely presenting proper advertisements to users will encourage them to click on search ads. With the rapid growth of advertising, there is a bidding mechanism that advertisers need to bid keywords on t...