Abstract: Web Personalization is viewed as an application of data mining and machine learning techniques to build models of user behavior that can be applied to the task of predicting user needs and adapting future interactions with the ultimate goal of improved user satisfaction. We start by providing a description of the personalization process and a classification of the current approaches to Web personalization. We discuss the various sources of data available to personalization systems, the modeling approaches employed and the current approaches to evaluating these systems. Recently a lot of research has been done on personalized applications, which are able to tailor the information presented to individual users. The main goal of these personalized systems is to learn the users needs without asking for it explicitly. In many approaches a web user profile is constructed that help to customize the information presented. Typically, the personal profiles are composed of the browsing data that was collected from the particular users previously. At the present time, such web user profiles already find application in various areas of information retrieval. They are employed to re-rank search results, modify user queries or assist during the retrieval process.
Keywords: Web Mining, Personalization, Collaborative, Content Mining, Structure Mining.
Title: An Evaluation of Personalization Systems using Web Mining Techniques
Author: K.Nirosha, V.Karthick, Mrs. E.Jaya,
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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