Generalized Second Price Auction with Probabilistic Broad Match

Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported broad match between queries and bid keywords when executing GSP auctions, however, it has been revealed that GSP auction with the standard broad-match mechanism they are currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g., its theoretical properties are known only for the single-slot case and full-information setting, and even in this simple setting, the corresponding worst-case social welfare can be rather bad). To address this issue, we propose a novel broad-match mechanism, which we call the Probabilistic Broad-Match (PBM) mechanism. Different from SBM that puts together the ads bidding on all the keywords matched to a given query for the GSP auction, the GSP with PBM (denoted as PBM-GSP) randomly samples a keyword according to a predefined probability distribution and only runs the GSP auction for the ads bidding on this sampled keyword. We perform a comprehensive study on the theoretical properties of the PBM-GSP. Specifically, we study its social welfare in the worst equilibrium, in both full-information and Bayesian settings. The results show that PBM-GSP can generate larger welfare than SBM-GSP under mild conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in Bayesian setting. To the best of our knowledge, this is the first work on broad-match mechanisms for GSP that goes beyond the single-slot case and the full-information setting

A Novel Method to Calculate Click Through Rate for Sponsored Search

Sponsored search adopts generalized second price (GSP) auction mechanism which works on the concept of pay per click which is most commonly used for the allocation of slots in the searched page. Two main aspects associated with GSP are the bidding amount and the click through rate (CTR). The CTR learning algorithms currently being used works on the basic principle of (#clicks_i/ #impressions_i) under a fixed window of clicks or impressions or time. CTR are prone to fraudulent clicks, resulting in sudden increase of CTR. The current algorithms are unable to find the solutions to stop this, although with the use of machine learning algorithms it can be detected that fraudulent clicks are being generated. In our paper, we have used the concept of relative ranking which works on the basic principle of (#clicks_i /#clicks_t). In this algorithm, both the numerator and the denominator are linked. As #clicks_t is higher than previous algorithms and is linked to the #clicks_i, the small change in the clicks which occurs in the normal scenario have a very small change in the result but in case of fraudulent clicks the number of clicks increases or decreases rapidly which will add up with the normal clicks to increase the denominator, thereby decreasing the CTR.Comment: 10 pages, 1 figur

"To Sponsor or not to Sponsor: Sponsored Search Auctions with Organic Links"

In 2010 sponsored search advertisements generated over $12 billion in revenue for search engines in the US market and accounted for 46% of online advertising revenue. A substantial portion of this revenue was generated by the sale of search keywords using auction mechanism. We analyze a game-theoretic model to understand the interplay between organic and sponsored links in keyword auctions. Our model allows both the relevance of the advertising firm as well as the position of its sponsored link to impact click-through-rates. Our results demonstrate how the presence of organic links (links generated by the search engine algorithm) may lead to either more or less aggressive bidding for sponsored link positions depending on consumers attitudes toward sponsored links and the extent to which sponsored and organic links are complements or substitutes. In contrast to equilibrium results in existing literature, the firm with the highest value per click does not necessarily win the first spot in the sponsored search listing. It also may be optimal for a firm to bid an amount greater than the expected value (or sale) from a click.sponsored search, organic search, online advertising, keyword auction

To Sponsor or Not to Sponsor: Sponsored Search Auctions with Organic Links

In 2010 sponsored search advertisements generated over $12 billion in revenue for search engines in the US market and accounted for 46% of online advertising revenue. A substantial portion of this revenue was generated by the sale of search keywords using an auction mechanism. We analyze a game-theoretic model to understand the interplay between organic and sponsored links in keyword auctions. Our model allows both the relevance of the advertising firm as well as the position of its sponsored link to impact click-through-rates. Our results demonstrate how the presence of organic links (links generated by the search engine algorithm) may lead to either more or less aggressive bidding for sponsored link positions depending on consumer attitudes toward sponsored links and the extent to which sponsored and organic links are complements or substitutes. In contrast to equilibrium results in existing literature, the …rm with the highest value per click does not necessarily win the first spot in the sponsored search listings. It also may be optimal for a firm to bid an amount greater than the expected value (or sale) from a click.

Hybrid Advertising Auctions

Several major websites offer hybrid auctions that allow advertisers to bid on a per-impression or a per-click basis. We present the first analysis of this hybrid advertising auction setting. The conventional wisdom is that brand advertisers (e.g. Coca-Cola) will bid per impression, while direct response advertisers (e.g. Amazon.com) will bid per click. We analyze a theoretical model of advertiser bidding to ask whether this conventional wisdom will hold up in practice. We find the opposite in a static game: brand advertisers bid per click, while direct response advertisers bid per impression. In a more realistic repeated game, we find that direct response advertisers bid per click, but brand advertisers may profitably alternate between bidding for clicks and bidding for impressions. The analysis implies that sellers of online advertising (a) may sometimes prefer not to offer advertisers multiple bidding options, (b) should try to ascertain advertisers' types when they do use hybrid auctions, and (c) should consider advertisers' strategic incentives when forming click-through rate expectations in hybrid auction formats

An Introduction to Mechanized Reasoning

Mechanized reasoning uses computers to verify proofs and to help discover new theorems. Computer scientists have applied mechanized reasoning to economic problems but -- to date -- this work has not yet been properly presented in economics journals. We introduce mechanized reasoning to economists in three ways. First, we introduce mechanized reasoning in general, describing both the techniques and their successful applications. Second, we explain how mechanized reasoning has been applied to economic problems, concentrating on the two domains that have attracted the most attention: social choice theory and auction theory. Finally, we present a detailed example of mechanized reasoning in practice by means of a proof of Vickrey's familiar theorem on second-price auctions

Multi-Keyword Multi-Click Option Contracts for Sponsored Search Advertising

In sponsored search, advertising slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, an auction mechanism encourages the advertisers to truthfully bid for keywords. However, keyword auctions have a number of problems including: (i) volatility in revenue, (ii) uncertainty in the bidding and charged prices for advertisers’ keywords, and (iii) weak brand loyalty between the advertiser and the search engine. To address these issues, we study the possibility of creating a special option contract that alleviates these problems. In our proposal, an advertiser purchases an option in advance from a search engine by paying an upfront fee, known as the option price. The advertiser then has the right, but no obligation, to then purchase specific keywords for a fixed costper-click (CPC) for a specified number of clicks in a specified period of time. Hence, the advertiser has increased certainty in sponsored search while the search engine could raise the customers’ loyalty. The proposed option contract can be used in conjunction with traditional keyword auctions. As such, the option price and corresponding fixed CPC price must be set such that there is no arbitrage opportunity. In this paper, we discuss an option pricing model tailored to sponsored search that deals with spot CPCs of targeted keywords in a generalized second price (GSP) auction. We show that the pricing model for keywords is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keywords) and is also multi-exercisable (multi-clicks). Experimental results on real advertising data verify our pricing model and demonstrate that advertising options can benefit both advertisers and search engines