Malone, T., Grant, K., Turbank, F., Brobst, S., & Cohen, M. Intelligent information-sharing systems. Communications of the ACM 30, 5 (1987), 390-402. Home/Search Document Not in Database Summary ACM TOC Related Articles Check |
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A Testing Methodology And Architecture For Computer Supported .. - Dugan Jr. (2000) (5 citations) (Correct)
....some of the important software systems that have been developed to implement them. Some Common CSCW Applications: A number of common computer applications fall under the domain of CSCW. Electronic mail consists of the asynchronous exchange of information between a sender and one or more recipients [17]. Newsgroups operate in a manner similar to electronic mail. Information is exchanged asynchronously between a sender and newsgroup covering a specific topic of interest through activity known as posting . Users interested in a particular newsgroup then download and read postings using a ....
Malone, T., et al., Intelligent Information Sharing Systems. Communications of the ACM, 1987. 30(5):390-402.
Information Immune Systems - Chao, Forrest (2003) (Correct)
....often require many examples before they can infer new rules. In contrast, an IIS using an instance based learning approach could potentially learn to block a class of data upon seeing only a single exemplar. Collaborative filtering uses the preferences of others to help an individual make choices [42, 24, 54]. A typical example is a system that recommends items to purchase based on individuals with a similar purchase history. By harnessing the collective preferences of many individuals, such systems can infer similarity between items without needing to understand the relationship between them. This ....
T. W. Malone, K. R. Grant, F. A. Turbak, S. A. Brobst, and M. D. Cohen, "Intelligent information sharing systems," Communications of the ACM, vol. 30, no. 5, pp. 390--402, 1987.
Information Immune Systems - Chao, Forrest (2002) (Correct)
....schemes often require many examples before they can infer new rules. In contrast, an IIS using an instance based learning approach could learn to block a class of data upon seeing only a single exemplar. Collaborative ltering uses the preferences of others to help an individual make choices [31, 16, 42]. For example, a collaborative ltering system would recommend an item for a person to purchase by choosing an item purchased by someone with a similar purchase history. By harnessing the collective preferences of many individuals, such systems can infer similarity between items without needing to ....
T. W. Malone, K. R. Grant, F. A. Turbak, S. A. Brobst, and M. D. Cohen. Intelligent information sharing systems. Communications of the ACM, 30(5):390-402, 1987.
Ephemeral and Persistent Personalization in Adaptive.. - Mizzaro, Tasso (2002) (2 citations) (Correct)
....portal (torii.sissa.it) on physics, which has been developed in the 5 th FP IST project TIPS (Tools for Innovative Publishing in Science) see tips.sissa.it. 4. Persistent Personalization in Information Filtering In previous work, we have developed and evaluated several content based filters [18] for persistent personalization. Among them [1, 20, 22] the most effective has been the information agent ifT (information filtering Tool) 20] which is based on the user modeling shell UMT (User Modeling Tool) 10] The work presented here concerns the exploitation of ifT in the Torii portal. ....
T. Malone, K. Grant, F. Turbak, S. Brobst, M. Cohen, Intelligent information sharing systems, Comm. of the ACM 43(8), 1987, 390-402.
Dynamic Information Filtering - Baudisch (2001) (1 citation) (Correct)
....can take the form of highlighting items of high importance or deleting items that are not considered relevant. Even though the term filtering has a literal connotation of leaving things out, we use it here in a more general sense that includes selecting things from a larger set of possibilities [MGT 87, p. 390] 1.1.1.1 Information filtering versus related research areas IF is similar to information retrieval (IR) in several ways [Koc74, Sal83] Both approaches involve the same basic components, the same flows of information, and can be accomplished with a very similar architecture [BC92] ....
.... techniques have been applied to several application areas including scientific publications [Luh58] technical memos and reports [FD92, FC97] Usenet news [FS91, Bac91, SK92, Ste92b, Bac92, JH92, SM93, Mae94, KHL 94, RIS 94, MS94, Lan95, YG95, Moc96, MRK97, MRK 97] electronic mail [Mye80, MGT 87, Pol88, GNOT92, Ter91, Ter93, LMM94] books [Ric79b, MR98] application program know how [LN98] the finding of experts [SW93, KSS96] Web pages [RM96, HT96, Bal97, THA 97, PB97, RP97, Bie98] classified ads [GGKS95] movies [Kay95, HRF95, AKK97, AKK98] music [Sha94, SM95, Loe92] and TV ....
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T.W. Malone, K.R. Grant, F.A. Turbak, S.A. Brobst, and M.D. Cohen. Intelligent information sharing systems. Communications of the ACM, 30(5):390-402, May 1987.
Information Filtering and Retrieval: An Overview - Riordan, Sorensen (Correct)
....results in a re ranking of the initially returned documents. 7 Collaborative Filtering Different criteria may be used to filter documents articles the filtering and retrieval systems mentioned so far in this paper all use the content of the documents as the basis for the filtering. Malone [26] describes three categories of filtering technique cognitive, social and economic. Cognitive filtering, as heretofore discussed, is based solely on the content of the articles. Social filtering techniques are based on the relationships between people and on their subjective judgments. ....
Malone, Grant, Turbak, Brobst, and Cohen. Intelligent information sharing systems. Communications of the ACM, 30(5):390--402, 1987.
Functions of a Web Warehouse - Cheng, Kambayashi, Lee, Mukesh (Correct)
....and maintain user profile. Monitoring users browsing or providing of key phrases that summarize user s interests can achieve generation of user profile. Comparing users profiles with contents of documents or items using similarity metric can filter documents or items. Thomas W. Malone et al. [9] classify filtering as the cognitive, social, and economic approaches. discusses the a web warehouse as a community oriented information repository between servers and clients to 1. Cognitive filtering or content based filtering. Several shortcomings have been pointed out on a pure contentbased ....
Thomas W. Malone, Kenneth R. Grant, Franklyn A. Turbak, Stephen A. Brobst, and Michael D. Cohen;. Intelligent Information-Sharing Systems. Communications of the ACM, 30(5):390 -- 402, May 1987.
Introducing Relevancy and Context Into an Open Hypermedia Link.. - Hughes (Correct)
....[25] whilst trying to move the field from just discussing the generation of information to include the reception of information. In 1987 Malone and others introduced three paradigms for information selection; cognitive, economic and social, whilst describing their system the Information Lens [48]. Cognitive filtering is now known as content based filtering. Economic selection takes into account the intellectual property cost of the information found and cost of transmission. It is now rising in importance as more Web resources require subscription. Examples include GroupLens [62] and ....
Thomas W. Malone, Kenneth R. Grant, Franklyn A. Turbak, Stephen A. Brobst, and Michael D. Cohen. Intelligent information-sharing systems. Communications of the ACM, 30(5):390--402, May 1987.
Rough Set Based Decision Model in Information Retrieval and.. - Li, Zhang, Swan (Correct)
....the users role specifically, identification of users interests, representation of interests, and application of such representations in interactions. The users just can describe their queries as vectors of terms or vectors of classes. At first, IF systems use AI based techniques such as rules [ 13 ] 6 ] to generate the profiles. Currently, most IF systems use machine learning techniques to generate user interest profiles [ 17 ] These techniques tend to obtain the correct weight for each element in the query vectors. The goals of IR and IF systems are both the estimation of the ....
T. W. Malone, K. R. Grant, F. A. Turbak, S.A., Brobst, and M. D. Cohen, Intelligent information sharing systems, Commun. ACM, 1987, 30: 390-402.
The Hidden Web - Kautz, Selman, Shah (1997) (7 citations) (Correct)
....model. As we argued earlier, there is reason to believe that referral chaining can be robust even against a significant degree of inaccuracy in the model. However, we will continue to explore this empirical question as use of the system grows. As mentioned previously, a number of social filtering (Malone et al. 1987) and recommendation systems (Resnick 1996) already exist, such as fiREFLY. REFERRAL WEB has a number of features that sets it apart from such systems: First, REFERRAL WEB attempts to uncover Common academic affiliation also provides a source of evidence for a relationship between individuals. ....
Malone, T. W.; Grant, K. R.; Turbak, F. A.; Brobst, S. A.; and Cohen, M. D. 1987. Intelligent Information Sharing Systems. Communications of the ACM 30(5): 390--402.
Understanding Email Use: Predicting Action on a.. - Human-Computer.. (2005) (Correct)
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Malone, T., Grant, K., Turbank, F., Brobst, S., & Cohen, M. Intelligent information-sharing systems. Communications of the ACM 30, 5 (1987), 390-402.
Minimizing Information Overload: The Ranking of Electronic.. - Losee, Jr. (1989) (6 citations) (Correct)
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T. W. Malone, K. R. Grant, F. A. Turbak, S. A. Brobst, and M. D. Cohen. Intelligent information sharing systems. Communications of the ACM, 30:390--402, 1987.
Pricing Electronic Mail to Solve - The Problem Of (Correct)
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Malone, T. W., Grant, K. R., Turbak, F. A., Brobst, S. A., &Cohen, M. D., (1987). Intelligent information-sharing systems. Communications of the ACM, 30(5), 390--402.
Markets for Attention: - Will Postage For (Correct)
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Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D., (1987). Intelligent information-sharing systems. Communications of the ACM, 30(5), 390-402.
Semantic Email: Theory and Applications - McDowell, Etzioni, Halevy (2004) (Correct)
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T. Malone, K. Grant, F. Turbak, S. Brobst, and M. Cohen. Intelligent informationsharing systems. Comm. of the ACM, 30(5):390--402, 1987.
Knowledge Pump: Supporting the Flow and Use of Knowledge - Glance, Arregui, Dardenne (1998) (14 citations) (Correct)
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Malone, T. W., Grant, K. R., Turbak, F. A., Brobst, S. A., Cohen, M. D. (1987): Intelligent Information Sharing Systems. Communications of the ACM 30:5, 390--402
Semantic Email - McDowell, Etzioni, Halevy, Levy (2004) (Correct)
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T. Malone, K. Grant, F. Turbak, S. Brobst, and M. Cohen. Intelligent information-sharing systems. Comm. of the ACM, 30(5):390--402, 1987.
Adaptive User Modelling in AthosMail - Jokinen, Kanto, Rissanen (2004) (Correct)
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Malone,T., Grant,K., Turbak, F., Brobst, S. and Cohen, M. 1987. Intelligent information sharing systems. Communications of the ACM, (30):390-402.
Adaptive Radio: Achieving Consensus Using Negative Preferences - Chao, Balthrop, Forrest (2004) (1 citation) (Correct)
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T. W. Malone, K. R. Grant, F. A. Turbak, S. A. Brobst, and M. D. Cohen. Intelligent information sharing systems. Communications of the ACM, 30(5):390--402, 1987.
Adaptive Radio: Achieving Consensus Using Negative Preferences - Chao, Balthrop, Forrest (2004) (1 citation) (Correct)
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T. W. Malone, K. R. Grant, F. A. Turbak, S. A. Brobst, and M. D. Cohen. Intelligent information sharing systems. Communications of the ACM, 30(5):390--402, 1987.
A Personal Email Assistant - Bergman, Griss, Staelin (2002) (4 citations) (Correct)
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Tom W. Malone.; Grant, K. R.; Turbak, F. A.; Brobst, S. A.; and Cohien, M. D. Intelligent information-sharing systems. Communications of the ACM 30:390-402, 1987.
Data Management Issues and Trade-Offs in CSCW Systems - Prakash, Shim, Lee (1999) (1 citation) (Correct)
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# T.W. Malone, K.R. Grant, F.A. Turbak, S.A. Brobst, and M.D. Cohen, "Intelligent Information Sharing Systems," Comm. ACM, pp. 390--402, 1987.
Ontology-Based User Profiles for Search and Browsing - Gauch, Chaffee, Pretschner (2002) (Correct)
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T. Malone, K. Grant, F. Turbak, S. Brobst and M. Cohen. Intelligent Information Sharing Systems. Communications of the ACM, (30), May 1987, pp. 390-402.
Markets for Attention: Will Postage for Email Help? - Kraut, Sunder, Morris.. (Correct)
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Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D., (1987). Intelligent information-sharing systems. Communications of the ACM, 30(5), 390-402.
Unknown - Kraut Shyam Sunder (Correct)
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Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D., (1987). Intelligent information-sharing systems. Communications of the ACM, 30(5), 390-402.
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