Briefs

March 11, 2009, University Park, Pa. -- In the world of search engines, clicks mean cash, and in a sluggish economy, companies can benefit by maximizing click-throughs to their Web sites from search engines.

Jim Jansen, associate professor of information sciences and technology, Penn State, and colleagues, developed a measurement to gauge customer satisfaction with search engine results. They hope this metric will aid companies in optimization and marketing.

"In some sense, this study is a first step in using neural networks in the analysis of user-system interactions for Web search data," Jansen said. "This research explores the online behaviors of users so that commercial search engine companies can utilize the data to improve click-through rates by designing more efficient retrieval and ranking algorithms."

Jansen, along with Ying Zhang, industrial and manufacturing engineering, Penn State and Amanda Spink, Queensland University of Technology, used search logs from Dogpile.com to find the factors that predicted increased or decreased user click-throughs. They reported their results in the March issue of the Journal of the American Society for Information Science and Technology.

Based on neural network data, the researchers were able to identify nine factors that can help predict future click-through rates, with five having positive effects, four having negative effects and one having no effect. The positive factors were: number of records in a search, sum of listing ranks, mean query length, browser type and query time. Negative factors were: number of organic links clicked, rate of vertical type, time of first query and log in time. User intent, whether they were looking for information, to make a transaction or to navigate somewhere, did not have a significant impact on predicting future click-throughs.

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