Collaborative value filtering on the Web

Abstract

Today's Internet search engines help users locate information based on the textual similarity of a query and potential documents. Given the large number of documents available, the user often finds too many documents, and even if the textual similarity is high, in many cases the matching documents are not relevant or of interest. Our goal is to explore other ways to decide if documents are "of value" to the user, i.e., to perform what we call "value filtering." In particular, we would like to capture access information that may tell us-within limits of privacy concerns-which user groups are accessing what data, and how frequently. This information can then guide users, for example, helping identify information that is popular, or that may have helped others before. This is a type of collaborative filtering or community-based navigation. Access information can either be gathered by the servers that provide the information, or by the clients themselves. Tracing accesses at servers is simple, but often information providers are not willing to share this information. We therefore are exploring client-side gathering. Companies like Alexa are currently using client gathering in the large. We are studying client gathering at a much smaller scale, where a small community of users with shared interest collectively track their information accesses. For this, we have developed a proxy system called the Knowledge Sharing System (KSS) that monitors the behavior of a community of users. Through this system we hope to: 1. Develop mechanisms for sharing browsing expertise among a community of users; and 2. Better understand the access patterns of a group of people with common interests, and develop good schemes for sharing this information.