Intelligent Techniques for Web Personalization
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BeschreibungWeb personalizationcan be de?ned as any set of actions that can tailor the Web experience to a particular user or set of users. The experience can be something as casualas browsinga Web site oras (economically)signi?cantas tradingstock or purchasing a car. The actions can range from simply making the presentation more pleasing to anticipating the needs of a user and providing customized and relevant information. To achieve e?ective personalization, organizations must rely on all available data, including the usage and click-stream data (re?e- ing user behavior), the site content, the site structure, domain knowledge, user demographics and pro?les. In addition, e?cient and intelligent techniques are needed to mine these data for actionable knowledge, and to e?ectively use the discovered knowledge to enhance the users' Web experience. These techniques must address important challenges emanating from the size and the heteroge- ity of the data, and the dynamic nature of user interactions with the Web. E-commerce and Web information systems are rich sources of di?cult pr- lems and challenges for AI researchers. These challenges include the scalability of the personalization solutions, data integration, and successful integration of techniques from machine learning, information retrievaland ?ltering, databases, agent architectures, knowledge representation, data mining, text mining, stat- tics, user modelling and human-computer interaction. Throughout the history of the Web, AI has continued to play an essential role in the development of Web-based information systems, and now it is believed that personalization will prove to be the "killer-app" for AI.
InhaltsverzeichnisIntelligent Techniques for Web Personalization.- Intelligent Techniques for Web Personalization.- User Modelling.- Modeling Web Navigation: Methods and Challenges.- The Traits of the Personable.- Addressing Users' Privacy Concerns for Improving Personalization Quality: Towards an Integration of User Studies and Algorithm Evaluation.- Recommender Systems.- Case-Based Recommender Systems: A Unifying View.- Improving the Performance of Recommender Systems That Use Critiquing.- Hybrid Systems for Personalized Recommendations.- Enabling Technologies.- Collaborative Filtering Using Associative Neural Memory.- Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendations.- Discovering Interesting Navigations on a Web Site Using SAM I .- Personalized Information Access.- Personalisation of Web Search.- The Compass Filter: Search Engine Result Personalization Using Web Communities.- Predicting Web Information Content.- Systems and Applications.- Mobile Portal Personalization: Tools and Techniques.- IKUM: An Integrated Web Personalization Platform Based on Content Structures and User Behavior.- A Semantic-Based User Privacy Protection Framework for Web Services.- Web Personalisation for Users Protection: A Multi-agent Method.
Untertitel: IJCAI 2003 Workshop, ITWP 2003, Acapulco, Mexico, August 11, 2003, Revised Selected Papers. 'Lecture Notes in Artificial Intelligence'. 2005. Auflage. Book. Sprache: Englisch.
Erscheinungsdatum: November 2005
Seitenanzahl: 340 Seiten