Adaptive Model of Personalized Searches using Query Expansion and Ant Colony Optimization in the Digital Library

Abstract

A system that can provide useful information for users will be able to increase user loyalty. Presentation of useful information can be done by providing the information needed by each user or personalization. Personalization is a form required when the system wants to interact with its users. A personalized system is built based on the needs of each user. In this research, a new personalization model was built for the interaction between digital library systems with users. The proposed model uses adaptive query expansion algorithm and ant colony optimization for searching documents in digital libraries.The model uses metadata to the search process. The metadata consists of titles, abstracts and keywords of the document. The User model is built to monitor user activities when selecting a document link (click) and visit page document (visit). Data of research uses 50 journal documents. The research results that the proposed model improves search scores by 60% and the order of the search documents up to 56%.