Web Search Results Exploration via Cluster-Based Views and Zoom-Based Navigation
Information seeking on the Web has become day-to-day routine for more than two billion human beings most of who use traditional keyword-based search engines. Developers of these search engines stress personalization, prediction of users' next actions and mistake correction. But they are still struggling with results presentation and support for users, who make atypical queries or who do not exactly know what they are looking for. We address these issues via a novel approach for exploring web repositories, which naturally combines user search activities - look up, learning and investigation. We achieve this via view-based navigation in hierarchical clusters and two-dimensional graphs of search results.
web navigation exploratory search adaptive views results clustering graph visualization semantic web