Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining

With the rapid development in World Wide Web (WWW) technology, the number of webpages and the volume of information content have been overwhelming. It becomes increasingly important to help users find relevant webpage and information more easily and quickly. This situation causes widespread attention in constructing adaptive websites which automatically reorganize the structure or content by learning from the users’ browsing behaviors, as such the usage of the websites is improved. In this study we propose a new formulation for the website structure optimization (WSO) problem based on a comprehensive survey of existing works and practice considerations. An enhanced tabu search (ETS) algorithm is proposed with advanced search features of multiple neighborhoods, adaptive tabu lists, dynamic tabu tenure, and multi-level aspiration criteria. The experimental result on 24 real-world problem instances shows that the proposed ETS algorithm can obtain a better value of web usage estimation than a genetic algorithm method. Moreover, ETS is computationally efficient due to the strategy that handles problem constraints on-the-fly when constructing the solution.