<p>The internet advertising market is growing much faster than any other advertising vertical. The technology for serving advertising online goes more and more towards automated processes that analyze the page content and the user’s preferences and then matches the ads with these parameters.</p><p>The task at hand was to research and find methods that could be suitable for matching web documents to ads automatically, build a prototype system, make an evaluation and suggest areas for further development. The goals of the system was high throughput, accurate ad matching and fast response times. A requirement on the system was that human input could only be done when adding ads into the system for the system to be scalable.</p><p>The prototype system is based on the vector space model and a td-idf weighting scheme. The cosines coefficient was used in the system to quantify the similarity between a web document and an ad.</p><p>A technique called stemming was also implemented in the system together with a clustering solution that aided the ad matching in cases where few matches could be done on the keywords attached to the ads. The system was built with a threaded structure to improve throughput and scalability.</p><p>The tests results show that you accurately can match ads to a website’s content using the vector space model and the cosines-coefficient. The tests also show that the stemming has a positive effect on the ad matching accuracy.</p>
| Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:liu-15878 |
| Date | January 2008 |
| Creators | Pettersson, Jimmie |
| Publisher | Linköping University, Department of Computer and Information Science |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, text |
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