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Com) recommendation provides suggestions of books according to the similarities in the user’s purchase history. , 2000). Eirinaki DQG9D]LUJLDQQLV GH¿QHD:HESHUVRQDOL]Dtion process in four steps: collecting Web data, modeling and categorizing the collected data, analyzing the collected data, and determining actions to be performed. In the following we give DQRYHUYLHZRIWKH¿UVWWKUHHSURFHVVHVZKLFK are essential steps before a personalized action can be performed. Web Data Web data can vary.
Such a domain image can be presented in the form of ontologies (Chen, 2006). RVDOD %ORFNHHO &DSWXULQJ the content of Web data in ontologies is the ¿UVWVWHSWRZDUGVWKHEXLOGLQJRI6HPDQWLF Web applications. Thus, natural language text is considered as the most important source data of ontologies for the Semantic Web (Maedche, 2003). Although techniques and tools for ontology construction have been developed over the last decade, they provide little support to EXLOGLQJRQWRORJLHVLQDQHI¿FLHQWHQJLQHHUing manner.
However, the design of a more appropriate context factor scale requires more efforts. An important issue with taking a hierarchical clustering approach is the need of pre-processing source data. Since existing clustering algorithms require input in numeric form, coding text and scaling them to numbers can result in the loss of semantics (“meaning”) in text. Therefore, careful pre-processing is essential. A common approach is to perform term extraction. , 2005). Another important issue is the post-processing of 22 resultant clusters, referred to as cluster labeling, a semantic problem.
Digital Watermarking Theoretic Foundation and Applications by Eggers
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