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.

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

This chapter describes how the adaptive web technologies discussed in this book have been applied to news access. First, we provide an overview of different types of adaptivity in the context of news access and identify corresponding algorithms. For each adaptivity type, we briefly discuss representative systems that use the described techniques. Next, we discuss an in-depth case study of a personalized news system. As part of this study, we outline a user modeling approach specifically designed for news personalization, and present results from an evaluation that attempts to quantify the effect of adaptive news access from a user perspective. We conclude by discussing recent trends and novel systems in the adaptive news space.

While personalization has proved to be an important supplement to web applications, the constraints of mobile information access make personalization essential to producing usable applications. Mobile devices, such as cell phones or personal digital assistants, have much smaller screens, more limited input capabilities, slower and less reliable network connections, less memory and less processing power than desktop computers. We discuss an adaptive personalization technology that automatically delivers personalized information available to the mobile user via wireless or wired synchronization on platforms such as AvantGo or Qualcomm’s BREWTM. Both of these platforms have capabilities not available in most browsers for wireless devices. In particular, they allow for local storage of some content on the mobile device that may be accessed without wireless connectivity. Our applications attempt to optimize the batch download of information to wireless devices so that the delays and costs associated with interactive browsing are reduced. We present evidence that the personalization algorithm increases the usage of mobile content applications by displaying personally relevant information to individual users.

landmark article, over a half century ago, Vannevar Bush envisioned a Memory Extender device he dubbed the (7). Bush's ideas anticipated and inspired numerous breakthroughs, including hypertext, the Internet, the World Wide Web, and Wikipedia. However, despite these triumphs, the memex has still not lived up to its potential in corporate settings. One reason is that corporate users often don't have sufficient time or incentives to contribute to a corporate memory or to explore others' contributions. At FXPAL, we are investigating ways to automatically create and retrieve useful corporate memories without any added burden on anyone. In this paper we discuss how ProjectorBox—a smart appliance for automatic presentation capture—and PAL Bar—a system for proactively retrieving contextually relevant corporate memories—have enabled us to integrate content from a variety of sources to create a cohesive multimedia corporate memory for our organization.

Technology abounds for capturing presentations. However, no simple solution exists that is completely automatic. ProjectorBox is a "zero user interaction" appliance that automatically captures, indexes, and manages presentation multimedia. It operates continuously to record the RGB information sent from presentation devices, such as a presenter's laptop, to display devices, such as a projector. It seamlessly captures high-resolution slide images, text and audio. It requires no operator, specialized software, or changes to current presentation practice. Automatic media analysis is used to detect presentation content and segment presentations. The analysis substantially enhances the web-based user interface for browsing, searching, and exporting captured presentations. ProjectorBox has been in use for over a year in our corporate conference room, and has been deployed in two universities. Our goal is to develop automatic capture services that address both corporate and educational needs.

Automatic lecture capture can help students, instructors, and educational institutions. Students can focus less on note-taking and more on what the instructor is saying. Instructors can provide access to lecture archives to help students study for exams and make-up missed classes. And online lecture recordings can be used to support distance learning. For these and other reasons, there has been great interest in automatically capturing classroom presentations. However, there is no simple solution that is completely automatic. ProjectorBox is our attempt to create a “zero user interaction” appliance that automatically captures, indexes, and manages presentation multimedia. It operates continuously to record the RGB information sent from presentation devices, such as an instructor’s laptop, to display devices such as a projector. It seamlessly captures high-resolution slide images, text, and audio. A web-based user interface allows students to browse, search, replay, and export captured presentations.

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.