WEBKDD 2002 - mining Web data for discovering usage patterns and profiles : 4th international workshop, Edmonton, Canada, july 23, 2002 : revised papers
1 WorkshopTheme Data mining as a discipline aims to relate the analysis of large amounts of user data to shed light on key business questions. Web usage mining in particular, a relatively young discipline, investigates methodologies and techniques that - dress the unique challenges of discovering in...
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| Format: | Livre numérique |
| Jezik: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serija: | Lecture notes in computer science. Lecture notes in artificial intelligence
2703 |
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| Online pristup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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| Edition sous un autre format: | • WEBKDD 2002, mining Web data for discovering usage patterns and profiles, 4th international workshop, Edmonton, Canada, july 23, 2002, revised papers, Osmar R. Zaïane, Jaideep Srivastava, Myra Spiliopoulou...[et al.] (eds.), Berlin, Springer, 2003, 1 vol. (VIII-179 p.), Lecture notes in computer science, 3-540-20304-4 • WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles, Texte imprimé, 9783662163016 |
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| 111 | 2 | |a WEBKDD 2002 |n (4th |c :Edmonton, Canada). | |
| 245 | 1 | 0 | |a WEBKDD 2002 - mining Web data for discovering usage patterns and profiles : |b 4th international workshop, Edmonton, Canada, july 23, 2002 : revised papers |c [edited by] Osmar R. Zaïane, Jaideep Srivastava, Myra Spiliopoulou[et al.]. |
| 260 | |a Berlin [etc.] : |b Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Lecture notes in computer science. Lecture notes in artificial intelligence |v 2703 |x 1611-3349 |x 2945-9141 | |
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| 505 | 0 | |a LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition -- Mining eBay: Bidding Strategies and Shill Detection -- Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models -- Web Usage Mining by Means of Multidimensional Sequence Alignment Methods -- A Customizable Behavior Model for Temporal Prediction of Web User Sequences -- Coping with Sparsity in a Recommender System -- On the Use of Constrained Associations for Web Log Mining -- Mining WWW Access Sequence by Matrix Clustering -- Comparing Two Recommender Algorithms with the Help of Recommendations by Peers -- The Impact of Site Structure and User Environment on Session Reconstruction in Web Usage Analysis. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 1 WorkshopTheme Data mining as a discipline aims to relate the analysis of large amounts of user data to shed light on key business questions. Web usage mining in particular, a relatively young discipline, investigates methodologies and techniques that - dress the unique challenges of discovering insights from Web usage data, aiming toevaluateWebusability,understandtheinterestsandexpectationsofusersand assess the e?ectiveness of content delivery. The maturing and expanding Web presents a key driving force in the rapid growth of electronic commerce and a new channel for content providers. Customized o?ers and content, made possible by discovered knowledge about the customer, are fundamental for the establi- ment of viable e-commerce solutions and sustained and e?ective content delivery in noncommercial domains. Rich Web logs provide companies with data about their online visitors and prospective customers, allowing microsegmentation and personalized interactions. While Web mining as a domain is several years old, the challenges that characterize data analysis in this area continue to be formidable. Though p- processing data routinely takes up a major part of the e?ort in data mining, Web usage data presents further challenges based on the di?culties of assigning data streams to unique users and tracking them over time. New innovations are required to reliably reconstruct sessions, to ascertain similarity and di?erences between sessions, and to be able to segment online users into relevant groups. | ||
| 650 | |a Informatique | ||
| 650 | |a Intelligence artificielle | ||
| 650 | |a Réseaux d'ordinateurs | ||
| 650 | |a Exploration de données | ||
| 650 | |a Informatique documentaire | ||
| 650 | |a Sites Web | ||
| 650 | |a Culture numérique | ||
| 650 | |a Actes de congrès | ||
| 700 | 1 | |a Zaïane, Osmar R. |4 pbd | |
| 700 | 1 | |a Srivastava, Jaideep, |d 19..- |4 pbd | |
| 700 | 1 | |a Spiliopoulou, Myra, |d 1965- |4 pbd | |
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| 776 | 0 | |t WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles |b Texte imprimé |z 9783662163016 | |
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