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IBM Cambridge Research Center

  Project: Collaborative Filtering: Using pointers from others

Researchers: David Maltz, Kate Ehrlich, Debra Cash, Alex Lee

A Collaborative User Experience Project:

Members of Lotus Research conducted two studies of filters, which are described below.

The first reviews a collaborative filtering system in which people point their colleagues to documents of interest. Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated. Current collaborative filtering systems provide tools for readers to filter documents based on aggregated ratings over a changing group of readers. Motivated by the results of a study of information sharing, we describe a different type of collaborative filtering system in which people who find interesting documents actively send "pointers" to those documents to their colleagues. A "pointer" contains a hypertext link to the source document as well as contextual information to help the recipient determine the interest and relevance of the document prior to accessing it. Preliminary data suggest that people are using the system in anticipated and unanticipated ways, as well as creating information "digests" that combine pointers with original text.

The second and more recent study deals with the issue of credibility. Access to information is necessary, but not sufficient, in allowing users to find what they need in electronic domains. Users also want value from the information; it should be perceived to be accurate, timely, or at least relevant to the user's question. The perceived credibility of a document depends on a number of factors that, to date, have been unexplored in the computer systems literature. This paper offers a framework for understanding current mediation and filtering strategies based on anonymous, expert and workgroup authors. It also describes the ways credibility of a source is ascertained formally by expert searchers with traditional librarianship skills and informally, by content area experts. These insights have both technical and business implications.

Related publications:
TR 94-08, David Maltz and Kate Ehrlich. Pointing the Way: Active Collaborative Filtering
TR 95-10, Kate Ehrlich and Debra Cash. Credibility, Trust, and Information Filtering