Difference between revisions of "Predicting ads' quality"

Latest revision as of 14:43, 7 February 2011

Internet advertising is the main source of income for search engines today. As the number of Internet users increases, the Internet advertising becomes increasingly popular among people who want to advertise a service or a product. Google reported $6,475 million revenue from advertisement in 2009 which is 8% more than the previous year. This, emphasizes the fact that internet advertising is a widely attractive and growing market for advertisers and search engines.

When a user enters a query in a search engine, there are often some sponsored links or ads presented alongside with the search results. These ads are chosen by an auction between all candidate ads which have keywords similar to the user entered query. In this auction, winners will be chosen based on two factors: offered bid and quality. In this article, quality means the ability to attract more users' clicks. Advertisers usually want to place their ads in the best spot in the page without paying more money, so they try to increase the quality of ads by choosing good terms for title and descriptions. On the other side, Search engines use the Price Per Click (PPC) model for Internet advertising. In this model search engines can earn money just if somebody clicks on the displayed ads and as a result, there is no cost for the advertisers merely because of ad appearance. So for earning maximum revenue, search engines also try to select ads with better quality to attract more clicks. Roughly speaking, ads with high quality are important for both advertiser and search engine.

For the ads which have been in the system for longer periods of the time, we can find their quality just by looking at their click through rate (CTR). If an ad had higher amount of CTR, it is more attractive to users and has better quality. But for new ads or for those ones without enough historical data, we should find another way to estimate their quality.

People

Mohamed Hefeeda

Hamed Sadeghi Neshat (MSc student)

On-going Research Problems

Estimating the click-through rate for new ads with semantic and feature based similarity algorithms

References and Links

some good references are available here

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-Internet advertising is the main source of income for search engines today. As the number of internet users increases, the internet advertising becomes more and more popular among people who want to advertise a service or a product. Google reported it had 6,475 million dollars revenue from advertisement in 2009 which is 8% more than previous year, and it means internet advertising is a wide and attractive market for advertisers and search engines.
+Internet advertising is the main source of income for search engines today. As the number of Internet users increases, the Internet advertising becomes increasingly popular among people who want to advertise a service or a product. Google reported $6,475 million revenue from advertisement in 2009 which is 8% more than the previous year. This, emphasizes the fact that internet advertising is a widely attractive and growing market for advertisers and search engines.
-Impressed ads around search result pages in search engines are chosen with an auction. It means when a user enters a search query, an auction will be placed among all relevant ads and top ads will be chosen based on two factors: offered bid and quality. Search engines use the Price per Click (PPC) model for internet advertising, in this model if ad placed in search result page for a search query, advertisers should pay only if user clicks on the ad, and otherwise there is no cost for advertisers just because of ad impression. So for earning maximum revenue, search engines try to select ads with most quality which can attract more clicks.
+When a user enters a query in a search engine, there are often some sponsored links or ads presented alongside with the search results. These ads are chosen by an auction between all candidate ads which have keywords similar to the user entered query. In this auction, winners will be chosen based on two factors: offered bid and quality. In this article, quality means the ability to attract more users' clicks. Advertisers usually want to place their ads in the best spot in the page without paying more money, so they try to increase the quality of ads by choosing good terms for title and descriptions. On the other side, Search engines use the Price Per Click (PPC) model for Internet advertising. In this model search engines can earn money just if somebody clicks on the displayed ads and as a result, there is no cost for the advertisers merely because of ad appearance. So for earning maximum revenue, search engines also try to select ads with better quality to attract more clicks. Roughly speaking, ads with high quality are important for both advertiser and search engine.
-The propose of current study is to investigate on a quality measurement method working with conceptual and feature based similarity algorithms which can find similar ads from historical data and estimate ads’ quality for new ads in compare with current ads in the Internet. More over, in this experiment we will examine some novel machine learning methods and use them in the internet and advertising concepts and try to customize them in order to be effective in these areas.
+For the ads which have been in the system for longer periods of the time, we can find their quality just by looking at their click through rate (CTR). If an ad had higher amount of CTR, it is more attractive to users and has better quality. But for new ads or for those ones without enough historical data, we should find another way to estimate their quality.
+== People ==
+* [http://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]
+* [http://www.cs.sfu.ca/~hsadeghi/ Hamed Sadeghi Neshat (MSc student)]
+== On-going Research Problems ==
+Estimating the click-through rate for new ads with semantic and feature based similarity algorithms
+== References and Links  ==
+some good references are available [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-projects/OnlineAds/ctrPrediction/references/resources%20for%20online%20ads/ here]