The Experts below are selected from a list of 9153 Experts worldwide ranked by ideXlab platform

Daqing He - One of the best experts on this subject based on the ideXlab platform.

  • ICEBE - Personalized Recommendation of E-Commerce Website Category Hierarchy Based on Web Usage Mining and Multidimensional Scaling
    2015 IEEE 12th International Conference on e-Business Engineering, 2015
    Co-Authors: Pengwu, Jiamin Wang, Daqing He
    Abstract:

    The purpose of this paper is to study personalized needs of e-commerce website Category Hierarchy based on users' mental models by means of Multidimensional Scaling and Web Usage Mining. The users' browsing Category paths in an e-commerce website is extracted based on the Web Usage Mining, and the Multidimensional Scaling was used to probe the structure and composition of the users' mental models of website Category Hierarchy based on their browsing Category paths, at last, users' personalized needs can be identified. Three million web log data records were collected for experimental study. The experimental results show the proposed method is efficient to discover users' personalized needs of expected Category Hierarchy based on large scale web log data automatically and efficiently.

  • Personalized Recommendation of E-Commerce Website Category Hierarchy Based on Web Usage Mining and Multidimensional Scaling
    2015 IEEE 12th International Conference on e-Business Engineering, 2015
    Co-Authors: Jiamin Wang, Daqing He
    Abstract:

    The purpose of this paper is to study personalized needs of e-commerce website Category Hierarchy based on users' mental models by means of Multidimensional Scaling and Web Usage Mining. The users' browsing Category paths in an e-commerce website is extracted based on the Web Usage Mining, and the Multidimensional Scaling was used to probe the structure and composition of the users' mental models of website Category Hierarchy based on their browsing Category paths, at last, users' personalized needs can be identified. Three million web log data records were collected for experimental study. The experimental results show the proposed method is efficient to discover users' personalized needs of expected Category Hierarchy based on large scale web log data automatically and efficiently.

Jiamin Wang - One of the best experts on this subject based on the ideXlab platform.

  • ICEBE - Personalized Recommendation of E-Commerce Website Category Hierarchy Based on Web Usage Mining and Multidimensional Scaling
    2015 IEEE 12th International Conference on e-Business Engineering, 2015
    Co-Authors: Pengwu, Jiamin Wang, Daqing He
    Abstract:

    The purpose of this paper is to study personalized needs of e-commerce website Category Hierarchy based on users' mental models by means of Multidimensional Scaling and Web Usage Mining. The users' browsing Category paths in an e-commerce website is extracted based on the Web Usage Mining, and the Multidimensional Scaling was used to probe the structure and composition of the users' mental models of website Category Hierarchy based on their browsing Category paths, at last, users' personalized needs can be identified. Three million web log data records were collected for experimental study. The experimental results show the proposed method is efficient to discover users' personalized needs of expected Category Hierarchy based on large scale web log data automatically and efficiently.

  • Personalized Recommendation of E-Commerce Website Category Hierarchy Based on Web Usage Mining and Multidimensional Scaling
    2015 IEEE 12th International Conference on e-Business Engineering, 2015
    Co-Authors: Jiamin Wang, Daqing He
    Abstract:

    The purpose of this paper is to study personalized needs of e-commerce website Category Hierarchy based on users' mental models by means of Multidimensional Scaling and Web Usage Mining. The users' browsing Category paths in an e-commerce website is extracted based on the Web Usage Mining, and the Multidimensional Scaling was used to probe the structure and composition of the users' mental models of website Category Hierarchy based on their browsing Category paths, at last, users' personalized needs can be identified. Three million web log data records were collected for experimental study. The experimental results show the proposed method is efficient to discover users' personalized needs of expected Category Hierarchy based on large scale web log data automatically and efficiently.

Pengwu - One of the best experts on this subject based on the ideXlab platform.

  • ICEBE - Personalized Recommendation of E-Commerce Website Category Hierarchy Based on Web Usage Mining and Multidimensional Scaling
    2015 IEEE 12th International Conference on e-Business Engineering, 2015
    Co-Authors: Pengwu, Jiamin Wang, Daqing He
    Abstract:

    The purpose of this paper is to study personalized needs of e-commerce website Category Hierarchy based on users' mental models by means of Multidimensional Scaling and Web Usage Mining. The users' browsing Category paths in an e-commerce website is extracted based on the Web Usage Mining, and the Multidimensional Scaling was used to probe the structure and composition of the users' mental models of website Category Hierarchy based on their browsing Category paths, at last, users' personalized needs can be identified. Three million web log data records were collected for experimental study. The experimental results show the proposed method is efficient to discover users' personalized needs of expected Category Hierarchy based on large scale web log data automatically and efficiently.

Xingshe Zhou - One of the best experts on this subject based on the ideXlab platform.

  • A hybrid Similarity measure of contents for TV personalization
    Multimedia Systems, 2010
    Co-Authors: Xingshe Zhou, Liang Zhou
    Abstract:

    Similarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and Category Hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and Category Hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval. © Springer-Verlag 2010.

  • A hybrid similarity measure of contents for TV personalization
    Multimedia Systems, 2010
    Co-Authors: Xingshe Zhou, Liang Zhou
    Abstract:

    International audienceSimilarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and Category Hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and Category Hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval. © Springer-Verlag 2010

  • A hybrid similarity measure of contents for TV personalization
    Multimedia Systems, 2010
    Co-Authors: Xingshe Zhou, Liang Zhou
    Abstract:

    Similarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and Category Hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and Category Hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval.

  • combining vector space model and Category Hierarchy model for tv content similarity measure
    Multimedia and Ubiquitous Engineering, 2009
    Co-Authors: Xingshe Zhou
    Abstract:

    In this paper, we propose a new approach for TV content similarity measure, which combines both vector space model and Category Hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the combining approach proposed here is superior to using either similarity measure alone for example-based retrieval of TV content.

  • MUE - Combining Vector Space Model and Category Hierarchy Model for TV Content Similarity Measure
    2009 Third International Conference on Multimedia and Ubiquitous Engineering, 2009
    Co-Authors: Xingshe Zhou
    Abstract:

    In this paper, we propose a new approach for TV content similarity measure, which combines both vector space model and Category Hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the combining approach proposed here is superior to using either similarity measure alone for example-based retrieval of TV content.

Sun Park - One of the best experts on this subject based on the ideXlab platform.

  • automatic e mail classification using dynamic Category Hierarchy and semantic features
    Iete Technical Review, 2010
    Co-Authors: Sun Park, Dong Un An
    Abstract:

    AbstractThe explosive increase in the use of e-mails has produced a large amount of information, and caused a problem in that many spam or regular e-mails with the same or similar contents are duplicated over and over day-to-day. We often group e-mails into categories in order to maintain e-mails efficiently. However, reading the e-mail messages and classifying them is still a tedious task. Moreover, the number of e-mails and its manual classification is increasing every day. So, e-mail users are demanding methods that can classify e-mails more and more efficiently. In this paper, we propose an e-mail multiCategory classification and e-mail message reorganization method using semantic features and a dynamic Category Hierarchy reconstruction method. The proposed method in this paper classifies multiCategory e-mails automatically and supports keyword and directory search methods in the classified results so that a large volume of e-mails can be managed efficiently. In addition, we used an interactive dynami...

  • automatic email multi Category classification using dynamic Category Hierarchy and non negative matrix factorization
    Journal of KIISE:Software and Applications, 2010
    Co-Authors: Sun Park
    Abstract:

    The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-Category classification is created a Category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-Category classification method using NMF for automatic Category label construction method and dynamic Category Hierarchy method for the reorganization of email messages in the Category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-Category email automatically, email messages in their Category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

  • e mail classification and Category re organization using dynamic Category Hierarchy and pca
    Journal of information and communication convergence engineering, 2009
    Co-Authors: Sun Park, Dong Un An
    Abstract:

    The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. We often group e-mails into categories for maintaining e-mail efficiently. However reading the email messages and classifying them is still tedious task. Moreover, the number of e-mails and manual classifying is increasing everyday. So, automatic e-mail classification is important techniques. In this paper, we propose a multi-way e-mail classification method that uses PCA for automatic Category generation and dynamic Category Hierarchy for re-organizing e-mail categories. It classifies a huge amount of receiving e-mail messages automatically, efficiently, and accurately.

  • automatic e mail Hierarchy classification using dynamic Category Hierarchy and principal component analysis
    The Journal of Advanced Navigation Technology, 2009
    Co-Authors: Sun Park
    Abstract:

    The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. Therefore, it is more required to classify incoming e-mails efficiently and accurately. Currently, the e-mail classification techniques are focused on two way classification to filter spam mails from normal ones based mainly on Bayesian and Rule. The clustering method has been used for the multi-way classification of e-mails. But it has a disadvantage of low accuracy of classification and no Category labels. The classification methods have a disadvantage of training and setting of Category labels by user. In this paper, we propose a novel multi-way e-mail Hierarchy classification method that uses PCA for automatic Category generation and dynamic Category Hierarchy for high accuracy of classification. It classifies a huge amount of incoming e-mails automatically, efficiently, and accurately.

  • AIS - E-mail classification agent using Category generation and dynamic Category Hierarchy
    Lecture Notes in Computer Science, 2005
    Co-Authors: Sun Park, Ju-hong Lee, Sangho Park, Jung-sik Lee
    Abstract:

    With e-mail use continuing to explode, the e-mail users are demanding a method that can classify e-mails more and more efficiently. The previous works on the e-mail classification problem have been focused on mainly a binary classification that filters out spam-mails. Other approaches used clustering techniques for the purpose of solving multi-Category classification problem. But these approaches are only methods of grouping e-mail messages by similarities using distance measure. In this paper, we propose of e-mail classification agent combining Category generation method based on the vector model and dynamic Category Hierarchy reconstruction method. The proposed agent classifies e-mail automatically whenever it is needed, so that a large volume of e-mails can be managed efficiently