LAUSR

Continually, search engines improve their capabilities toward facilitating search and the retrieval enhancement. Despite the great efforts in the information retrieval field, the retrieved results may be out of user’s expectation. This may be due to the huge number of web resources, and unidentified user’s interests and domain. This paper proposes exploiting social annotations for improving retrieval based on personalization. The personalization focuses on web resources and retrieval process. In this context, new layer of knowledge is added to the web resource analysis and retrieval. Then, the additional knowledge leads to improve the retrieved results to be close to user’s interests. So, it retrieved different results for the same query based on the user’s interests. By applying the system, the experiments realize 36% precision improvement compared to non-personalized search engine. Moreover, the user satisfaction measured by evaluating search results versus user’s priorities, where it was in between 92% up to 100%.