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Using collaborative filtering to weave an information tapestry

Published on Dec 1, 1992in Communications of The ACM5.41
· DOI :10.1145/138859.138867
David A. Goldberg27
Estimated H-index: 27
(PARC),
David A. Nichols5
Estimated H-index: 5
(PARC)
+ 1 AuthorsDouglas B. Terry19
Estimated H-index: 19
(PARC)
Cite
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.
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  • References (11)
  • Citations (2886)
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References11
Newest
Published on Jun 2, 1992 in SIGMOD (International Conference on Management of Data)
Douglas B. Terry19
Estimated H-index: 19
(PARC),
David A. Goldberg27
Estimated H-index: 27
(PARC)
+ 1 AuthorsBrian M. Oki2
Estimated H-index: 2
(PARC)
In a database to which data is continually added, users may wish to issue a permanent query and be notified whenever data matches the query. If such continuous queries examine only single records, this can be implemented by examining each record as it arrives. This is very efficient because only the incoming record needs to be scanned. This simple approach does not work for queries involving joins or time. The Tapestry system allows users to issue such queries over a database of mail and bulleti...
Published on Dec 2, 1990in ACM Sigois Bulletin
Ernst Lutz1
Estimated H-index: 1
,
Hans V. Kleist-Retzow1
Estimated H-index: 1
,
Karl Hoernig1
Estimated H-index: 1
The main goal in the design of intelligent information processing systems is to provide automatic support for the processing of the large amount of documents, today's office workers are confronted with. Existing message filtering systems can provide this support only if the messages to be processed are at least semi-structured.The MAFIA System (MAil-FIlter-Agent) overcomes these limitations of existing message filtering systems by providing an automatic document classification component which re...
Published on Jan 1, 1990
Douglas B. Terry1
Estimated H-index: 1
Published on Aug 29, 1988 in VLDB (Very Large Data Bases)
Jack Kent1
Estimated H-index: 1
,
Douglas B. Terry1
Estimated H-index: 1
,
Willie-Sue Orr1
Estimated H-index: 1
A database management system provides the ideal support for electronic mail applications. The Walnut mail system built at the Xerox Palo Alto Research Center was recently redesigned to take better advantage of its underlying database facilities. The ability to pose ad-hoc queries with a "fill-in-the-form" browser allows people to browse their mail quickly and effectively, while database access paths guarantee fast retrieval of stored information. Careful consideration of the systems' usage was r...
Published on Jul 1, 1988in ACM Transactions on Information Systems2.63
Stephen Pollock1
Estimated H-index: 1
(bell northern research)
Much computerized support for knowledge workers has consisted of tools to handle low-level functions such as distribution, storage, and retrieval of information. However, the higher level processes of making decisions and taking actions with respect to this information have not been supported to the same degree. This paper describes the ISCREEN prototype system for screening text messages. ISCREEN includes a high-level interface for users to define rules, a component that screens text messages, ...
Published on Oct 1, 1987
Jonathan Rosenberg7
Estimated H-index: 7
(CMU: Carnegie Mellon University),
Craig F. Everhart2
Estimated H-index: 2
(CMU: Carnegie Mellon University),
Nathaniel S. Borenstein13
Estimated H-index: 13
(CMU: Carnegie Mellon University)
lIntroduction This paper provides an overview of the Andrew Message System, which is in operation within the Andrew project at Carnegie Mellon University. The Andrew environment currently consists of 300 highfunction workstations (typified by the IBM RT-PC) each running Berkeley Unix and attached to a large campus-wide network. A central file system provides transparently the appearance of a large, monolithic Unix file system. In addition, there are approximately 600 IBM PC’s and 300 (University...
Published on May 1, 1987in Communications of The ACM5.41
Thomas W. Malone53
Estimated H-index: 53
(MIT: Massachusetts Institute of Technology),
Kenneth R. Grant6
Estimated H-index: 6
(MIT: Massachusetts Institute of Technology)
+ 2 AuthorsMichael D. Cohen27
Estimated H-index: 27
(UM: University of Michigan)
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 supporting the problem-solving activities of groups.
Published on Jan 1, 1987
M. Darnovsky1
Estimated H-index: 1
,
G. W. Bowman1
Estimated H-index: 1
Published on Dec 1, 1985 in SOSP (Symposium on Operating Systems Principles)
David K Gifford D K59
Estimated H-index: 59
(MIT: Massachusetts Institute of Technology),
Robert W. Baldwin2
Estimated H-index: 2
(MIT: Massachusetts Institute of Technology)
+ 1 AuthorsJohn M. Lucassen5
Estimated H-index: 5
(MIT: Massachusetts Institute of Technology)
Published on Nov 1, 1984
Jacob Palme13
Estimated H-index: 13
Cited By2886
Newest
Published on Jan 1, 2020
Nevena Vranić , Pavle Milošević5
Estimated H-index: 5
+ 1 AuthorsBratislav Petrovic5
Estimated H-index: 5
Published on Jan 1, 2020
Monika Arora , Akanksha Bansal Chopra (DU: University of Delhi), Veer Sain Dixit5
Estimated H-index: 5
(DU: University of Delhi)
This paper aims at highlighting the increasing role of artificial intelligence in business and making familiar its various aspects vis-a-vis its immediate requirement in the present Indian business scenario. The paper takes into account the aspects of blockchain and deep learning components with regard to the business as future of artificial intelligence in business scenario. The study also includes the benefits and challenges of the use of artificial intelligence in business with influence of b...
Published on Jan 1, 2020
Urvi Mitra (Amity University), Garima Srivastava (Amity University)
In this day and age of information overload, it becomes difficult to filter information that is relevant to our needs. World Wide Web is the largest database of information available to us. It stores information using the hypertext paradigm, i.e., interlinking web pages through hyperlinks, which users can click on to access related information. An agent acting on behalf of humans, can make the task of sifting through information to find what we need easier for us. This paper focuses on the appli...
Published on Nov 1, 2019in Expert Systems With Applications4.29
Junpeng Guo (College of Management and Economics), Jiangzhou Deng (College of Management and Economics), Yong Wang (CQUPT: Chongqing University of Posts and Telecommunications)
Abstract In general, a practical online recommendation system does not rely on only one algorithm but adopts different types of algorithms to predict user preferences. Although most of similarity measures can rapidly calculate the similarity on the basis of co-rated items, their prediction accuracy is not satisfactory in the case of sparse datasets. Making full use of all the rating information can effectively improve the recommendation quality, but it reduces the system efficiency because all t...
Published on Oct 1, 2019in Pattern Recognition5.90
Xu Yu2
Estimated H-index: 2
,
Feng Jiang2
Estimated H-index: 2
(QUST: Qingdao University of Science and Technology)
+ 1 AuthorsDunwei Gong2
Estimated H-index: 2
Abstract Cross-domain collaborative filtering, which transfers rating knowledge across multiple domains, has become a new way to effectively alleviate the sparsity problem in recommender systems. Different auxiliary domains are generally different in the importance to the target domain, which is hard to evaluate using previous approaches. Besides, most recommender systems only take advantage of information from user- or item-side auxiliary domains. To overcome these drawbacks, we propose a cross...
Published on 2019in arXiv: Information Retrieval
Mostafa Khalaji1
Estimated H-index: 1
(KNTU: K.N.Toosi University of Technology),
Chitra Dadkhah
Nowadays, Recommender Systems have become a comprehensive system for helping and guiding users in a huge amount of data on the Internet. Collaborative Filtering offers to active users based on the rating of a set of users. One of the simplest and most comprehensible and successful models is to find users with a taste in recommender systems. In this model, with increasing number of users and items, the system is faced to scalability problem. On the other hand, improving system performance when th...
View next paperHybrid Recommender Systems: Survey and Experiments