Reviewing Personalizing Filtering Approaches in Web

Abstract

Huge volume of online content in the era of web 2.0 increases difficulties in seeking information. Users are unable to get the right information based on their needs and preferences. Information filtering is capable to overcome the problems of information overload by filtering irrelevant information. There has been much work done in this area to increase the quality of recommendation to users based on their needs. This paper presents an overview of information filtering approaches that classified into rule-based, content-based, collaborative filtering and hybrid method. A categorization personalization overview is proposed comprises of user profiling and filtering approaches. This paper also discusses various advantages, limitations and future trends in information filtering approaches.