Foltz, P. and Dumais, S. (1992). Personalized information delivery: An analysis of information ltering methods. Communications of the ACM, 35:51-60. Home/Search Document Not in Database Summary ACM TOC Related Articles Check |
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New Techniques In Intelligent Information Filtering - Macskassy (2003) (Correct)
....used as though they were real ratings in a collaborative filtering system. 1.3 Acquiring the User Profile This thesis will focus on the use of content based filters. As mentioned in the previous section, currently used acquisition methods involve asking the user for a list of keywords [HK70, FD92] observing the user through normal day usage [MK93, AFJM95, Lie95, BP99] or by asking the user for relevance feedback on sample data [Roc71, SB90, FD92, SSS98, BP99] These techniques work well with user interests that are based on criteria that are directly evaluable: seeing a piece of ....
....filters. As mentioned in the previous section, currently used acquisition methods involve asking the user for a list of keywords [HK70, FD92] observing the user through normal day usage [MK93, AFJM95, Lie95, BP99] or by asking the user for relevance feedback on sample data [Roc71, SB90, FD92, SSS98, BP99] These techniques work well with user interests that are based on criteria that are directly evaluable: seeing a piece of information, a user can directly assess whether it is interesting. But what if the criterion is not directly evaluable Business news provides an illustration ....
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Peter W. Foltz and Susan T. Dumais. Personalized information delivery: An analysis of information filtering methods. Communications of the ACM, 35(12):51--60, Dec 1992.
Profile-Driven Cache Management - Cherniack, Galvez, Franklin, Zdonik (Correct)
....general form of predicates over its channels, and is therefore closer to our notion of the domain specification portion of a profile. User profiles for text based data have been extensively investigated in the context of Information Filtering and Selective Dissemination of Information research [12]. The systems in these areas use techniques from the Information Retrieval (IR) world for filtering unstructured text based documents [4] In general, IR profile systems use either a Boolean model or a similarity based model. In the Boolean model a user profile is constructed by combining keywords ....
P. W. Foltz and S. T. Dumias. Personalized information delivery: an analysis of information filtering methods. Communications of the ACM, December 1992.
Web personalization using Extended Boolean Operations with Latent.. - Nakov (2000) (Correct)
....personalized search engine could return information only from the eld of interest to the user. The same way a well personalized Web site changes dynamically its content in order to present to the user only relevant information, news or advertisements, according to the previously created pro le. [10] Asking explicitly the users for some kind of relevance feedback may not always be the best way to create their pro les. This is especially the case when using key words. As Furnas, Landauer, Gomez and Dumais have shown in [11] people use the same words to describe the same subject in 10 20 of ....
Foltz, P. W. and Dumais, S. T.: Personalized information delivery: An analysis of information ltering methods. Communications of the ACM, 35(12). Experiment using LSI for information ltering. (1992) 51-60
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....ever changing contextual knowledge and foster collaboration while capturing, representing and interpreting the knowledge resources of their organisations. Most information filtering techniques are based on the notion of user profile, which describes what information is of interest to the user [13]. Information is matched against a given user profile in order to determine whether the information is close enough to the user s interest to be selected. Information filtering traditionally focuses on the relationship between a given user profile and information at hand. On the other hand, an ....
Foltz, P. W., and Dumais, S. T. (1992). "Personalized information delivery: An analysis of information filtering methods." Communications of the ACM, 35(12), 51-60.
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Foltz, P. and Dumais, S. (1992). Personalized information delivery: An analysis of information ltering methods. Communications of the ACM, 35:51-60.
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Foltz, P. W. and Dumais, S. T.: 1992, Personalized information delivery: An analysis of information ltering methods, Communications of the ACM 35(12), 5160.
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P.W. Foltz and S.T. Dumais. Personalized information delivery: an analysis of information filtering methods. Communications of ACM, 35(12):51--60, 1992.
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P.W. Foltz and S.T. Dumais. Personalized information delivery: an analysis of information filtering methods. Communications of ACM, 35(12):51--60, 1992.
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Foltz, P. W. and Dumais, S. T. (1992) Personalized information delivery: An analysis of information filtering methods. Communications of the ACM, 35(12), 51-60. Experiment using LSI for information filtering.
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Foltz, P., Dumais, S. Personalized Information Delivery: An Analysis of Information Filtering Methods. In Communications of the ACM, 35(12) (December 1992), 5160.
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Foltz, P. and Dumais, S. (1992) Personalized information delivery: An analysis of information-filtering methods. Comm. of the ACM, 35 (12):51-60.
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P. W. Foltz and S. T. Dumais. Personalized information delivery: An analysis of information filtering methods. Communications of the ACM, 35(12):51--60, 1992.
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Foltz, P.W. and S.T. Dumais. Personalized Information Delivery: An Analysis of Information Filtering Methods. Communications of the ACM 35(12): 51-60, 1992. It does not mean that syntax plays no role. Its role could be to reduce the cognitive load in the semantic processing of text. 16
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Foltz P.W., Dumais S.T. : Personalized Information Delivery: An Analysis of Information Filtering Methods. In Comminucations of the ACM, 35(12), (1992) 51-60 (http://wwwpsych. nmsu.edu/~pfoltz/cacm/cacm.html)
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Foltz, P. W., Dumais, S. T., (1992), Personalized Information Delivery: An Analysis of Information Filtering Methods, Commun. ACM, 35(12), 51 -- 60
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Peter W. Foltz and Susan T. Dumais. Personalized information delivery: An analysis of information filtering methods. Communications of the ACM, 35(12):51-60, December 1992.
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