M. Perkowitz and O. Etzioni. Towards adaptive Web sites: conceptual framework and case study. Computer Networks (Amsterdam, Netherlands: 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....approach is that learning is unsupervised, i.e. no labeled data is required. More recently, systems such as PHOAKS [50] and our own # ##[25] 24] have sought to use cooperative information retrieval techniques for personalization. End to End personalization is predicated on adaptive Web sites[39], 38] which change the information returned in response to a user request based on the user. Very primitive forms of this can be seen in sites that ask the users to provide some basic information such as address, phone number, and keywords indicating interest, and then tailor their information ....

....indicating interest, and then tailor their information content (and especially ads) based on zip code, area code, demographic pro le, etc. However, in general the appearance of a particular page, including links on it, can also be changed when Web sites are adaptive. For example, Etzioniet al... [39] de ne operations such as promotions demotions, highlighting and linking that could be done on static pages to create content tailored for a speci c user dynamically. While interesting, their formalism seems to demand a robust clustering technique in order to work successfully. Moreover, factors ....

M. Perkowitz and O. Etzioni. Towards adaptiveweb sites: Conceptual framework and case study. Arti cial Intelligence, 118:245-275, 2000.

....an input parameter, however Perkowitz developed a method to automatically determine the value of K. They ran the K means algorithm multiple times, starting with a large value and gradually decreasing it. They were able to efficiently determine a good value for K during the clustering of web pages [10]. Our algorithm only needs to cluster strongly connected words, but the K means algorithm divides whole words into K clusters without removing weak relations. COBWEB is a incremental conceptual clustering algorithm. Each cluster records the probability of each attribute and value, and the ....

....News Dude, in our approach we model a continuum of long term to short term interests. Syskill Webert [9] use a predefined profile, which significantly increases the classification accuracy on previously unseen web pages. They emphasize the importance of a user profile. Perkowitz and Etzioni [10] introduced SCML, a concept learning algorithm that only extracts some concepts in a set of data. 3. USER INTEREST HIERARCHY A user interest hierarchy (UIH) organizes a user s general to specific interests. Towards the root of a UIH, more general (longer term) interests are represented by larger ....

Perkowitz, M., and Etzioni, O. Towards adaptive Web sites: Conceptual framework and case study, Artificial Intelligence 118, 245-275, 2000.

....15] suggest analyzing user access patterns to help design better web pages, sites, and browsers. Perkowitz et al. 15] proposed the design of adaptive web sites by promoting and demoting pages, highlighting links, adding hotlinks, and clustering related pages. Two years later, Perkowitz et al. [16] presented the PageGater algorithm, which analyzes user access logs in order to identify candidate link sets to be included in index pages. Their algorithm performs the following steps: a) process the access log into visits, b) nd clusters of linked pages and create an adjacency matrix, c) nd ....

Mike Perkowitz and Oren Etzioni. Towards Adaptive Web Sites: Conceptual Framework and Case Study. In Proceedings of the Eigth International WWW Conference, Toronto, Canada, May 1999. http://www8.org/w8-papers/2bcustomizing /towards/towards.html.

....Pirolli et al. 33] propose to create aggregation of Web pages according to their importance or their content. Perkowitz et al. 30] propose the design of adaptive Web sites by promoting and demoting pages, highlighting hyperlinks, adding hyperlinks and clustering related pages. Perkowitz et al. [31] present an algorithm that analyses user access logs in order to identify candidate hyperlink clusters to be included in index pages. The performance of the algorithm is measured according to the quality of the clusters; specifically, they assess the quality of a cluster by answering the following ....

Mike Perkowitz and Oren Etzioni. Towards adaptive Web sites: Conceptual framework and case study. Artijcial Inteligence, 118(1-2):245-275, 2000.

....Pirolli et al. 4] propose to create aggregation of Web pages according to their importance or their content. Perkowitz et al. 5] propose the design of adaptive Web sites by promoting and demoting pages, highlighting hyperlinks, adding hyperlinks and clustering related pages. Perkowitz et al. [6] present an algorithm that analyzes user access logs in order to identify candidate hyperlink clusters to be included in index pages. The performance of the algorithm is measured according to the quality of the clusters; speci cally, they assess the quality of a cluster by answering the following ....

M. Perkowitz and O. Etzioni (1999), Towards adaptive Web sites: Conceptual framework and case study, Arti cial Intelligence, vol. 118, no. 1-2, pp. 245-275.

....an input parameter, however Perkowitz developed a method to automatically determine the value of K. They ran the K means algorithra multiple times, starting with a large value and gradually decreasing it. They were able to efficiently determine a good value for K during the clustering of web pages [10]. Our algorithm only needs to cluster strongly connected words, but the K means algorithm divides whole words into K clusters without removing weak relations. COBWEB is a incremental conceptual clustering algorithm. Each cluster records the probability of each attribute and value, and the ....

....News Dude, in our approach we model a continuum of long term to short term interests. Syskill Webert [9] use a predefined profile, which significantly increases the classification accuracy on previously unseen web pages. They emphasize the importance of a user profile. Perkowitz and Etzioni [10] introduced SCML, a concept leaming algorithm that only extracts some concepts in a set of data. 3. USER INTEREST HIERARCHY A user interest hierarchy (UIH) organizes a user s general to specific interests. Towards the root of a UIH, more general (longer term) interests are represented by larger ....

Perkowitz, M., and Etzioni, O. Towards adaptive Web sites: Conceptual framework and case study, Artificial Intelligence 118,245-275, 2000.

....[65, 66] consider the costs and benefits of alerting users in different ways versus deferring messages to later, by considering a user s context and the time dependent utility of different messaging actions. 3. 3 User Modeling with Relational Markov Models As part of our work on Adaptive Websites [119, 120, 5] we have developed several learning algorithms for acquiring predictive models of user behavior from observations. Our initial work [4] evaluated the suitability of previous techniques, including Naive Bayes mixture models, first order, second order, and propositional Markov models. While a ....

M. Perkowitz and O. Etzioni. Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence, To appear, 2000.

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M. Perkowitz and O. Etzioni. Towards adaptive Web sites: conceptual framework and case study. Computer Networks (Amsterdam, Netherlands: 1999.

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M. Perkowitz and O. Etzioni. Towards adaptive Web sites: conceptual framework and case study. Computer Networks (Amsterdam, Netherlands: 1999), 31(11--16):1245--1258, 1999

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M. Perkowitz and O. Etzioni. Towards adaptive Web sites: conceptual framework and case study. Computer Networks (Amsterdam, Netherlands: 1999.

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Mike Perkowitz, Oren Etzioni, "Towards Adaptive Web Sites: Conceptual Framework and Case Study", Artificial Intelligence, Vol. 118, No. 1--2, pp. 245--275, 2000. 39

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Mike Perkowitz, Oren Etzioni, "Towards Adaptive Web Sites: Conceptual Framework and Case Study", Computer Networks, Vol. 31, No. 11--16 (Proc. of WWW8), pp. 1245--1258, May 1999.

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M. Perkowitz and O. Etzioni. Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence, 118:245--275, 2000.

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Perkowitz, M. and Etzioni, O. (1999) Towards adaptive web sites: conceptual framework and case study. Computer Networks (and ISDN Systems), 31. Proc. 8th Int. World Wide Web Conf., Toronto, Canada, 11--14 May, pp. 1245--1258. Elsevier Science, Netherlands.

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M. Perkowitz and O. Etzioni. Towards Adaptive Web Sites: Conceptual Framework and Case Study. In Proceedings of the 8th International World Wide Web Conference/Computer Networks, 31(1116) :1245--1258, 1999.

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Perkowitz, M., Etzioni, O.: Towards adaptive Web sites: conceptual framework and case study. WWW8. (1999)

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Mike Perkowitz and Oren Etzioni. Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence, 118(1-2), 2000.

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M. Perkowitz, O. Etzioni, Towards Adaptive Web Sites: Conceptual Framework and Case Study, in Proc. of WWW8, 1999

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Mike Perkowitz, Oren Etzioni, "Towards Adaptive Web Sites: Conceptual Framework and Case Study", Artificial Intelligence, Vol. 118, No. 1--2, pp. 245--275, 2000. 39

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Mike Perkowitz, Oren Etzioni, "Towards Adaptive Web Sites: Conceptual Framework and Case Study", Computer Networks, Vol. 31, No. 11--16 (Proc. of WWW8), pp. 1245--1258, May 1999.

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Perkowitz M., Etzioni O.: Towards Adaptive Web Sites: Conceptual Framework and Case Study. Computer Networks 31, Proceedings of the 8th International WWW Conference (1999)

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Perkowitz, M., and Etzioni, O., Towards adaptive web sites: conceptual framework and case study, Proc. 8th WWW Conf., 1999.

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M. Perkovitz, O. Etzioni, Towards adaptive web sites: Conceptual framework and case study, Artificial Intelligence 118, (2000) 245275.

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Mike Perkowitz and Oren Etzioni. Towards adaptive Web sites: conceptual framework and case study. Computer Networks (Amsterdam, Netherlands: 1999), 31(11--16):1245--1258, 1999.

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Mike Perkowitz and Oren Etzioni. Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence, 118:245--275, 2000.

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