The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
T Aoki - One of the best experts on this subject based on the ideXlab platform.
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intelligent web site understanding the Visitor Behavior
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2004Co-Authors: Juan D Velasquez, Pablo A Estevez, Hiroshi Yasuda, T Aoki, Eduardo S VeraAbstract:Intelligent web site is a new portal generation, able to improve its structure and content based on the analysis of the user Behavior. This paper focuses on modeling the Visitor Behavior, assuming that the only source available is his/her browsing Behavior. A framework to acquire and maintain knowledge extracted from web data is introduced. This framework allows to give online recommendations about the navigation steps, as well as offline recommendations for changing the structure and contents of the web site. The proposed methodology is applied to the web site of a commercial bank.
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a new similarity measure to understand Visitor Behavior in a web site
IEICE Transactions on Information and Systems, 2004Co-Authors: Juan D Velasquez, Hiroshi Yasuda, T Aoki, Richard WeberAbstract:The Behavior of Visitors browsing in a web site offers a lot of information about their requirements and the way they use the respective site. Analyzing such Behavior can provide the necessary information in order to improve the web site’s structure. The literature contains already several suggestions on how to characterize web site usage and to identify the respective Visitor requirements based on clustering of Visitor sessions. Here we propose to combine Visitor Behavior with the content of the respective web pages and the similarity between different page sequences in order to define a similarity measure between different visits. This similarity serves as input for clustering of Visitor sessions. The application of our approach to a bank’s web site and its Visitor sessions shows its potential for internet-based businesses. key words: web mining, browsing Behavior, similarity measure, clustering.
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using self organizing feature maps to acquire knowledge about Visitor Behavior in a web site
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2003Co-Authors: Juan D Velasquez, Hiroshi Yasuda, T Aoki, Richard Weber, Eduardo S VeraAbstract:When a user visits a web site, important information concerning his/her preferences and Behavior is stored implicitly in the associated log files. This information can be revealed by using data mining techniques and can be used in order to improve both, content and structure of the respective web site.
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Combining the Web content and usage mining to understand the Visitor Behavior in a Web site
Third IEEE International Conference on Data Mining, 2003Co-Authors: J. Velasquez, Hiroshi Yasuda, T AokiAbstract:A Web site is a semi structured collection of different kinds of data, whose motivation is to show relevant information to a Visitor and in this way capture her/his attention. Understanding the specific preferences that define the Visitor Behavior in a Web site is a complex task. An approximation is supposed that depends on the content, navigation sequence and time spent in each page visited. These variables can be extracted from the Web log files and the Web site itself, using Web usage and content mining respectively. Combining the described variables, a similarity measure among Visitor sessions is introduced and used in a clustering algorithm, which identifies groups of similar sessions, allowing the analysis of Visitor Behavior. In order to prove the methodology's effectiveness, it was applied in a certain Web site, showing the benefits of the described approach.
Hiroshi Yasuda - One of the best experts on this subject based on the ideXlab platform.
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intelligent web site understanding the Visitor Behavior
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2004Co-Authors: Juan D Velasquez, Pablo A Estevez, Hiroshi Yasuda, T Aoki, Eduardo S VeraAbstract:Intelligent web site is a new portal generation, able to improve its structure and content based on the analysis of the user Behavior. This paper focuses on modeling the Visitor Behavior, assuming that the only source available is his/her browsing Behavior. A framework to acquire and maintain knowledge extracted from web data is introduced. This framework allows to give online recommendations about the navigation steps, as well as offline recommendations for changing the structure and contents of the web site. The proposed methodology is applied to the web site of a commercial bank.
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a new similarity measure to understand Visitor Behavior in a web site
IEICE Transactions on Information and Systems, 2004Co-Authors: Juan D Velasquez, Hiroshi Yasuda, T Aoki, Richard WeberAbstract:The Behavior of Visitors browsing in a web site offers a lot of information about their requirements and the way they use the respective site. Analyzing such Behavior can provide the necessary information in order to improve the web site’s structure. The literature contains already several suggestions on how to characterize web site usage and to identify the respective Visitor requirements based on clustering of Visitor sessions. Here we propose to combine Visitor Behavior with the content of the respective web pages and the similarity between different page sequences in order to define a similarity measure between different visits. This similarity serves as input for clustering of Visitor sessions. The application of our approach to a bank’s web site and its Visitor sessions shows its potential for internet-based businesses. key words: web mining, browsing Behavior, similarity measure, clustering.
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using self organizing feature maps to acquire knowledge about Visitor Behavior in a web site
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2003Co-Authors: Juan D Velasquez, Hiroshi Yasuda, T Aoki, Richard Weber, Eduardo S VeraAbstract:When a user visits a web site, important information concerning his/her preferences and Behavior is stored implicitly in the associated log files. This information can be revealed by using data mining techniques and can be used in order to improve both, content and structure of the respective web site.
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Combining the Web content and usage mining to understand the Visitor Behavior in a Web site
Third IEEE International Conference on Data Mining, 2003Co-Authors: J. Velasquez, Hiroshi Yasuda, T AokiAbstract:A Web site is a semi structured collection of different kinds of data, whose motivation is to show relevant information to a Visitor and in this way capture her/his attention. Understanding the specific preferences that define the Visitor Behavior in a Web site is a complex task. An approximation is supposed that depends on the content, navigation sequence and time spent in each page visited. These variables can be extracted from the Web log files and the Web site itself, using Web usage and content mining respectively. Combining the described variables, a similarity measure among Visitor sessions is introduced and used in a clustering algorithm, which identifies groups of similar sessions, allowing the analysis of Visitor Behavior. In order to prove the methodology's effectiveness, it was applied in a certain Web site, showing the benefits of the described approach.
J. Velasquez - One of the best experts on this subject based on the ideXlab platform.
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Combining the Web content and usage mining to understand the Visitor Behavior in a Web site
Third IEEE International Conference on Data Mining, 2003Co-Authors: J. Velasquez, Hiroshi Yasuda, T AokiAbstract:A Web site is a semi structured collection of different kinds of data, whose motivation is to show relevant information to a Visitor and in this way capture her/his attention. Understanding the specific preferences that define the Visitor Behavior in a Web site is a complex task. An approximation is supposed that depends on the content, navigation sequence and time spent in each page visited. These variables can be extracted from the Web log files and the Web site itself, using Web usage and content mining respectively. Combining the described variables, a similarity measure among Visitor sessions is introduced and used in a clustering algorithm, which identifies groups of similar sessions, allowing the analysis of Visitor Behavior. In order to prove the methodology's effectiveness, it was applied in a certain Web site, showing the benefits of the described approach.
Rania Khalaf - One of the best experts on this subject based on the ideXlab platform.
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business process mining from e commerce web logs
Business Process Management, 2013Co-Authors: Nicolas Poggi, Vinod Muthusamy, David Carrera, Rania KhalafAbstract:The dynamic nature of the Web and its increasing importance as an economic platform create the need of new methods and tools for business efficiency. Current Web analytic tools do not provide the necessary abstracted view of the underlying customer processes and critical paths of site Visitor Behavior. Such information can offer insights for businesses to react effectively and efficiently. We propose applying Business Process Management (BPM) methodologies to e-commerce Website logs, and present the challenges, results and potential benefits of such an approach. We use the Business Process Insight (BPI) platform, a collaborative process intelligence toolset that implements the discovery of loosely-coupled processes, and includes novel process mining techniques suitable for the Web. Experiments are performed on custom click-stream logs from a large online travel and booking agency. We first compare Web clicks and BPM events, and then present a methodology to classify and transform URLs into events. We evaluate traditional and custom process mining algorithms to extract business models from real-life Web data. The resulting models present an abstracted view of the relation between pages, exit points, and critical paths taken by customers. Such models show important improvements and aid high-level decision making and optimization of e-commerce sites compared to current state-of-art Web analytics.
Juan D Velasquez - One of the best experts on this subject based on the ideXlab platform.
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intelligent web site understanding the Visitor Behavior
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2004Co-Authors: Juan D Velasquez, Pablo A Estevez, Hiroshi Yasuda, T Aoki, Eduardo S VeraAbstract:Intelligent web site is a new portal generation, able to improve its structure and content based on the analysis of the user Behavior. This paper focuses on modeling the Visitor Behavior, assuming that the only source available is his/her browsing Behavior. A framework to acquire and maintain knowledge extracted from web data is introduced. This framework allows to give online recommendations about the navigation steps, as well as offline recommendations for changing the structure and contents of the web site. The proposed methodology is applied to the web site of a commercial bank.
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a new similarity measure to understand Visitor Behavior in a web site
IEICE Transactions on Information and Systems, 2004Co-Authors: Juan D Velasquez, Hiroshi Yasuda, T Aoki, Richard WeberAbstract:The Behavior of Visitors browsing in a web site offers a lot of information about their requirements and the way they use the respective site. Analyzing such Behavior can provide the necessary information in order to improve the web site’s structure. The literature contains already several suggestions on how to characterize web site usage and to identify the respective Visitor requirements based on clustering of Visitor sessions. Here we propose to combine Visitor Behavior with the content of the respective web pages and the similarity between different page sequences in order to define a similarity measure between different visits. This similarity serves as input for clustering of Visitor sessions. The application of our approach to a bank’s web site and its Visitor sessions shows its potential for internet-based businesses. key words: web mining, browsing Behavior, similarity measure, clustering.
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using self organizing feature maps to acquire knowledge about Visitor Behavior in a web site
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2003Co-Authors: Juan D Velasquez, Hiroshi Yasuda, T Aoki, Richard Weber, Eduardo S VeraAbstract:When a user visits a web site, important information concerning his/her preferences and Behavior is stored implicitly in the associated log files. This information can be revealed by using data mining techniques and can be used in order to improve both, content and structure of the respective web site.
