No abstract available.

We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, a prototype application, in which the users try to find interesting scientific articles by browsing their titles. Implicit feedback is inferred from eye movement signals, with discriminative hidden Markov models estimated from existing data in which explicit relevance feedback is available. Collaborative filtering is carried out using the User Rating Profile model, a state-of-the-art probabilistic latent variable model, computed using Markov Chain Monte Carlo techniques. For new document titles the prediction accuracy with eye movements, collaborative filtering, and their combination was significantly better than by chance. The best prediction accuracy still leaves room for improvement but shows that proactive information retrieval and combination of many sources of relevance feedback is feasible.

In Western languages, text is traditionally presented in horizontal lines. However, reading of vertically arranged text might be more efficient because of the elimination of horizontal eye movements. We investigated the effect of vertical arrangement upon reading text presented on a computer screen. Even though vertically aligned text was read at a slower rate than horizontally aligned text, the difference was smaller than in previous studies. Analysis of eye-movement data revealed that there were no differences in fixation numbers and numbers of regressions between vertical formats and the standard-text format. But fixation durations were shorter for the standard-text format than for the vertical formats. Taken together, the results indicate that reading vertically presented text from a display device may be nearly as efficient as reading normal horizontal text. Therefore, the fact that text is normally vertically arranged in small-screen devices is not a usability problem.