Review Aspects of Using Social Annotation for Enhancing Search Engine Performance

Recently, search engines have improved to be more efficient in supporting user’s search process. Although they enhanced their capabilities to support user, still searcher spend long times in navigation. This is due to the different nature of users, where users have changeable interest and different culture, domain, and expressions. So, for improving search and make it closed to user’s expectation; user’s preferences have to be discovered. Nowadays, Information Retrieval researchers concern with Personalized Search which provides user’s preferences discovering. In this contribution, many efforts put path extracting user’s preferences through follow their behaviors, and action. Recently, researches focus on social annotations as additional metadata that may be used for extracting user’s preferences and interests. This paper reviews different aspects of using social annotation (as additional metadata) for enhancing search engines capabilities. Moreover, it especially focuses on personalized search which became today part of web 3.0 improvements. So, it proposes to categorize efforts in this field into two parts. The first concerns with improving personalized search by extracting user’s interests, and the second is for supporting personalized search by linking search phases to standard model.