Information gatekeepers, such as Internet search engines, travel experts, comparison shopping systems, credit raters, radio deejays, and movie critics, are an essential entry point for many information search and decision making tasks. They make recommendations on these tasks based on their expertise, but also frequently due to sponsorship by interested merchants. We develop and analyze a tractable model in which consumers may prefer or dislike the use of sponsored results in the recommendations, merchants' value for sponsorship increases with the gatekeeper's user base, and when there are negative externalities among merchants competing for consumers' attention. The optimal strategy strikes a balance between sponsorship revenues from merchants and user-based revenues. The gatekeeper may employ sponsored recommendations even when doing so is detrimental to users, or may not present enough sponsored results even when these improve the quality of recommendations. Product innovations or better domain expertise give the gatekeeper greater flexibility in using sponsored results.

No abstract available.

The practice of sponsored search advertising—where advertisers pay a fee to appear alongside particular Web search results—is now one of the largest and fastest growing source of revenue for Web search engines. We model and compare several mechanisms for allocating sponsored slots, including stylized versions of those used by Overture and Google, the two biggest brokers of sponsored search. The performance of these mechanisms depends on the degree of correlation between providers’ willingness to pay and their relevance to the search term. Ranking providers based on the product of relevance and bid price performs well and is robust across varying degrees of correlation. Ranking purely based on bid price fares nearly as well when bids and relevance are positively correlated (the expected regime), and is further enhanced by adding an editorial filter. Regardless of the allocation mechanism, sponsored search revenues are lower when users’ attention decays quickly at lower ranks, emphasizing the need to develop better user interfaces and control features. The search engine can address initial inscience of relevance scores by modifying rank allocations over time as it observes clickthroughs at each rank. We propose a rank-revision strategy that weights clicks on lower ranked items more than clicks on higher ranked items. This method is shown to converge to the optimal (maximum revenue) ordering faster and more consistently than other methods.

Web sites such as Internet search engines, web portals, and comparison shopping services, aim to provide information or recommendations to users who might be searching for information or trying to make a purchase decision. Paid placement advertising has established itself as an important revenue resource for such information-oriented web sites, which often deliberately bias their recommendations (or sequence of results) in return for a fee from providers who wish to get preferential placement on the results page. This article examines the paid-placement ranking strategies of the two dominant firms in this industry, and compares their revenues under different scenarios via computational simulation. We find that ranking paid placement links by the product of willingness to pay and relevance is better, in most cases, than ranking by willingness to pay alone, which performs best only when the correlation between the provider's relevance and willingness to pay is large. We also analyze the impact of the competition for placement slots on placement revenues under these mechanisms.

Internet search engines and comparison shopping have recently begun implementing a paid placement strategy, where some content providers are given prominent positioning in return for a placement fee. This bias generates placement revenues but creates a disutility to users, thus reducing user-based revenues. We formulate the search engine design problem as a tradeoff between these two types of revenues. We demonstrate that the optimal placement strategy depends on the relative benefits (to providers) and disutilities (to users) of paid placement. We compute the optimal placement fee, characterize the optimal bias level, and analyze sensitivity of the placement strategy to various factors. In the optimal paid placement strategy, the placement revenues are set below the monopoly level due to its negative impact on advertising revenues. An increase in the search engine's quality of service allows it to improve profits from paid placement, moving it closer to the ideal. However, an increase in the value-per-user motivates the gatekeeper to increase market share by reducing further its reliance on paid placement and fraction of paying providers.