Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Contextualizing the blogosphere: A comparison of traditional and novel user interfaces for the web

In this paper, we investigate how contextual user interfaces affect blog reading experience. Based on a review of previous research, we argue why and how contextualization may result in (H1) enhanced blog reading experiences. In an eyetracking experiment, we tested 3 different web-based user interfaces for information spaces. The StarTree interface (by Inxight) and the Focus-Metaphor interface are compared with a standard blog interface. Information tasks have been used to evaluate and compare task performance and user satisfaction between these three interfaces. We found that both contextual user interfaces clearly outperformed the traditional blog interface, both in terms of task performance as well as user satisfaction. © 2007 Laqua, S., Ogbechie, N. and Sasse, M. A

Measuring children's search behaviour on a large scale

Children often experience problems during information-seeking using traditional search interfaces and search technologies, that are designed for adults. This is because children engage with the world in fundamentally different ways than adults. To design search technologies that support children in effective and enjoyable information-seeking, more research is needed to examine children’s specific skills and needs concerning information-seeking. Therefore, we developed an application that can monitor children’s search behaviour on a large scale. In this paper, we present the steps taken to develop this application. The basis of the application is UsaProxy, an existing system that is used to monitor the user’s usage of websites. We have increased the accuracy of UsaProxy and have developed an application that is able to extract useful information from UsaProxy’s log files