Most of the information on the Web is inherently structured, product pages of large online shopping sites such as Amazon.com being a typical example. Yet, unstructured keyword queries are still the most common way to search for such structured information, producing an ambiguities and poor ranking, and by that degrading user experience. This problem can be resolved by query segmentation, that is, transformation of unstructured keyword queries into structured queries. The resulting queries can be used to search product databases more accurately, and improve result presentation and query suggestion. The main contribution of our work is a novel approach to query segmentation based on unsupervised machine learning. Its highlight is that query and click-through logs are used for training. Extensive experiments over a large query and click log from a leading shopping engine demonstrate that our approach significantly outperforms baseline.

We describe preliminary results of experiments with an unsupervised framework for query segmentation, transforming keyword queries into structured queries. The resulting queries can be used to more accurately search product databases, and potentially improve result presentation and query suggestion. The key to developing an accurate and scalable system for this task is to train a query segmentation or attribute detection system over labeled data, which can be acquired automatically from query and click-through logs. The main contribution of our work is a new method to automatically acquire such training data - resulting in significantly higher segmentation performance, compared to previously reported methods.

Proactive contextual information systems help people locate information by automatically suggesting potentially relevant resources based on their current tasks or interests. Such systems are becoming increasingly popular, but designing user interfaces that effectively communicate recommended information is a challenge: the interface must be unobtrusive, yet communicate enough information at the right time to provide value to the user. In this paper we describe our experience with the FXPAL Bar, a proactive information system designed to provide contextual access to corporate and personal resources. In particular, we present three features designed to communicate proactive recommendations more effectively: translucent recommendation windows increase the user's awareness of particularly highly-ranked recommendations, query term highlighting communicates the relationship between a recommended document and the user's current context, and a novel recommendation digest function allows users to return to the most relevant previously recommended resources. We present empirical evidence supporting our design decisions and relate lessons learned for other designers of contextual recommendation systems.

People routinely rely on physical and electronic systems to remind themselves of details regarding personal and organizational contacts. These systems include rolodexes, directories and contact databases. In order to access details regarding contacts, users must typically shift their attention from tasks they are performing to the contact system itself in order to manually look-up contacts. This paper presents an approach for automatically retrieving contacts based on users' current context. Results are presented to users in a manner that does not disrupt their tasks, but which allows them to access contact details with a single interaction. The approach promotes the discovery of new contacts that users may not have found otherwise and supports serendipity.

We describe a user interface for wireless information devices, specifically designed to facilitate learning about users' individual interests in daily news stories. User feedback is collected unobtrusively to form the basis for a content-based machine learning algorithm. As a result, the described system can adapt to users' individual interests, reduce the amount of information that needs to be transmitted, and help users access relevant information with minimal effort.