Name
Abstract | ||
|---|---|---|
We consider negotiations between publishers and advertisers in a marketplace for ads. Motivated by Google's online PrintAds system which is such a marketplace, we focus on the role of the market runner in improving market efficiency. We abstract the problem of pricing guidance where the market runner provides an initial price-point for negotiations based on data analysis. The problem is nuanced because the market runner can not fully reveal the price data for any of the publishers. We introduce two solutions for pricing guidance, the first using clustering and the second using support vector machines, and present experimental evaluation of our methods. Pricing guidance by the market runner is a novel direction, and we expect more research in the future. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1592748.1592757 | KDD Workshop on Data Mining and Audience Intelligence for Advertising |
Keywords | Field | DocType |
market runner,support vector machine,novel direction,price data,printads example,market efficiency,initial price-point,ad sale negotiation,pricing guidance,data analysis,present experimental evaluation,online printads system,machine learning,ranking | Ranking,Advertising,Computer science,Support vector machine,Market efficiency,Operations research,Revenue optimization,Cluster analysis,Negotiation | Conference |
Citations | PageRank | References |
1 | 0.62 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adam Isaac Juda | 1 | 1 | 0.62 |
| S. Muthukrishnan | 2 | 8025 | 734.98 |
| Ashish Rastogi | 3 | 161 | 10.55 |