As the market for neural network software has become more and more competitive, I’ve been intrigued to watch the proliferation of applications for this breed of statistical analysis. In a week that has produced news of neural networks put to use to diagnose epilepsy, pick stocks, protect children from internet pornography, and predict wind power came a particularly intriguing item that might not get the attention it deserves: Penn State information sciences professor Jim Jansen and his colleague Amanda Spink of the Queensland University used a neural net for a study of internet search engines and user satisfaction. Because click-throughs mean potential sales for businesses that rely on internet advertising, the study could send search engine developers scrambling to retool their engines.
Here was the question Jansen and Spink posed: what is it about the results a search engine produces that causes the person who receives them to click through on a particular result?
Here was how they went about answering that question: they obtained data on 7 million interactions from the search aggregator Dogpile and used neural networks software to classify the purpose of the search–e.g., information gathering, navigation help–used other statistical analysis methods to relate their classifications to the number of click-throughs. Here a click-through indicates that the user was satisfied enough with the result to pursue it further, and obviously a search engine that produces more click-through responses is more commercially desirable.
I’ll be interested to see the reaction to the study from the search engine community, but in the meantime I’ll certainly be more aware of click-throughs in my work on commercial websites.