Journal ArticleDOI
Using collaborative filtering to weave an information tapestry
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TLDR
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.Abstract:
The Tapestry experimental mail system developed at the Xerox Palo Alto Research Center is 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.read more
Citations
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Journal ArticleDOI
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
References
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Journal ArticleDOI
Intelligent information-sharing systems
TL;DR: The Information Lens system is a prototype intelligent information-sharing system that is designed to include not only good user interfaces for supporting the problem-solving activity of individuals, but also good organizational interfaces for support the problem -solving activities of groups.
Proceedings ArticleDOI
Continuous queries over append-only databases
TL;DR: The techniques used in Tapestry are described, which do not depend on triggers and thus be implemented on any commercial database that supports SQL and are applicable to any append-only database.
Journal ArticleDOI
A rule-based message filtering system
TL;DR: The ISCREEN prototype system for screening text messages includes a high-level interface for users to define rules, a component that screens text messages, and a conflict detection component that examines rules for inconsistencies.
Proceedings ArticleDOI
An overview of the Andrew message system
TL;DR: An overview of the Andrew Message System, which is in operation within the Andrew project at Carnegie Mellon University, and a central file system provides transparently the appearance of a large, monolithic Unix file system.
Journal ArticleDOI
ACM president's letter: electronic junk
TL;DR: The visibility of personal computers, individual workstations, and local networks has focused most of the attention on generating information; it is now time to focus more attention on receiving information--the processes of controlling and filtering information that reaches the persons who must use it.