The amount of information available on the World Wide Web is growing at an unprecedented rate, making it very difficult for users to find interesting information. This situation is likely worsen in the future unless the end user has the available tools to assist them. Web personalization is a research area which has received great attention in recent years. Web personalization aims to assist the users with information overload problem. One area of web personalization is the so called recommender systems. Recommender systems make recommendations based on the user’s individual profiles. Traditionally, the user profiles are keyword-based, they work on the premise that, those items which match certain keywords found in the user’s profile will be of interest and of relevance to the user, so those items are recommended to the user. One of the problems with the keyword-based profile representation methods is that a lot of useful information is lost during the pre-processing phase. To overcome this problem eliciting and utilization of semantic-based information from the domain, rather than the individual keywords, within all stages of the personalization process including can enhance the personalization process. This chapter presents a state-of-the-art survey of the techniques which can be used to semantically enhance the data processing, user modeling and the recommendation stages of the personalization process.
|Title of host publication||E-commerce and E-services 2008|
|Editors||Ting I-Hsien, Wu Hui-Ju|
|Number of pages||19|
|Publication status||Published - 2008|