Collaborative filters help people make choices based on the opinions of other people. GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles. News reader clients display predicted scores and make it easy for users to rate articles after they read them. Rating servers, called Better Bit Bureaus, gather and disseminate the ratings. The rating servers predict scores based on the heuristic that people who agreed in the past will probably agree again. Users can protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction. The entire architecture is open: alternative software for news clients and Better Bit Bureaus can be developed independently and can interoperate with the components we have developed.

predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering by recording their reactions to documents they read. The reactions are called annotations; they can be accessed by other people’s filters. Tapestry is intended to handle any incoming stream of electronic documents and serves both as a mail filter and repository; its components are the indexer, document store, annotation store, filterer, little box, remailer, appraiser and reader/browser. Tapestry’s client/server architecture, its various components, and the Tapestry query language are described.

Information dissemination is a powerful mechanism for finding information in wide-area environments. An information dissemination server accepts long-term user queries, collects new documents from information sources, matches the documents against the queries, and continuously updates the users with relevant information. This paper is a retrospective of the Stanford Information Filtering Service (SIFT), a system that as of April 1996 was processing over 40,000 worldwide subscriptions and over 80,000 daily documents. The paper describes some of the indexing mechanisms that were developed for SIFT, as well as the evaluations that were conducted to select a scheme to implement. It also describes the implementation of SIFT, and experimental results for the actual system. Finally, it also discusses and experimentally evaluates techniques for distributing a service such as SIFT for added performance and availability. Note to Referees: This paper contains material from three earlier...

Social filtering systems that use explicit ratings require a large number of ratings to remain viable. The effort involved for a user to rate a document may outweigh any benefit received, leading to a shortage of ratings. One approach to this problem is to use implicit ratings: where user actions are recorded and a rating is inferred from the recorded data. This paper discusses the costs and benefits of using implicit ratings for information filtering applications.

This paper describes a new approach for dealing with the vocabulary problem in human-computer interaction. Most approaches to retrieving textual materials depend on a lexical match between words in users' requests and those in or assigned to database objects. Because of the tremendous diversity in the words people use to describe the same object, lexical matching methods are necessarily incomplete and imprecise [5]. The latent semantic indexing approach tries to overcome these problems by automatically organizing text objects into a semantic structure more appropriate for matching user requests. This is done by taking advantage of implicit higher-order structure in the association of terms with text objects. The particular technique used is singular-value decomposition, in which a large term by text-object matrix is decomposed into a set of about 50 to 150 orthogonal factors from which the original matrix can be approximated by linear combination. Terms and objects are represented by 50 to 150 dimensional vectors and matched against user queries in this “semantic” space. Initial tests find this completely automatic method widely applicable and a promising way to improve users' access to many kinds of textual materials, or to objects and services for which textual descriptions are available.

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Abstract. The role of models in the design of computer systems to support interpersonal and cooperative work is examined. It is argued that the current generation of models over-emphasise determinism at the expense of interpretation in the work process. It is further argued that there are many cases in which designs pass between many different professional groups (office workers, managers, analysts, designers, programmers). Each of these groups has its own worldview and specialised language, and hence they are termed "semantic communities". When designs pass between semantic communities, something is lost and something is gained-- but the objects on which each community works are not commensurable. The distinct objects of work (office problems, analyses, designs, programs) do not map onto each other, and cannot be mutually tested using simple truelfalse criteria. This is termed a problem of "ontological drift", and arises whenever several distinct semantic communities work on the "same " system. It is suggested that the disparity so often observed between design expectations and the ways systems are actually used is therefore quite normal. Current efforts are directed at eliminating the disparity. We suggest that a more fruitful approach might be to accept that the final determination of a system rests with the users. In the long run this might give rise to different types of design principles than those used at the moment. In the short run, even the consciousness of this perspective could make significant differences to design dialogues and attitudes to "users".

Interfaces to databases have traditionally been designed as single-user systems that hide other users and their activity. This paper aims to show that collaboration is an important aspect of searching online information stores that requires explicit computerised support. The claim is made that a truly user-centred system must acknowledge and support collaborative interactions between users. Collaborative working implies a need to share information: both the search product and the search process. Searches need not be restricted to inanimate resources but people can also search for other people. The ARIADNE system is introduced as an example of computerised support for collaboration between browsers. A number of systems offering varied approaches to supporting collaboration are surveyed and a structure for analysing the various aspects of collaboration is applied.

this article, a filtering model is proposed that decomposes the overall task into subsystem functionalities and highlights the need for multiple adaptation techniques to cope with uncertainties. A filtering system, SIFTER, has been implemented based on the model, using established techniques in information retrieval and artificial intelligence. These techniques include document representation by a vector-space model, document classification by unsupervised learning, and user modeling by reinforcement learning. The system can filter information based on content and a user's specific interests. The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents. We also describe experimental studies conducted with SIFTER to filter computer and information science documents collected from the Internet and commercial database services. The experimental results demonstrate that the system performs very well in filtering documents in a realistic problem setting.

This paper introduces the special issue of Knowledge-Based Systems on HumanComputer Collaboration (HCC). It derives a set of fundamental issues from a definition of collaboration, introduces two major approaches to HCC, and surveys each approach, showing how it formulates and addresses the issues. It concludes by proposing some themes that should characterize a unified approach to human-computer collaboration. 1 Introduction Collaboration is a process in which two or more agents work together to achieve shared goals. Thirty researchers came together in Raleigh, North Carolina in October of 1993 for a AAAI Fall Symposium dedicated to this topic. The goal of the symposium was to achieve a better understanding of Human-Computer Collaboration (HCC), collaboration involving at least one human and one computational agent. In particular, the symposium sought to explore the fundamental nature of collaborative problem solving, understand the constraints brought to bear by the differing charac...