Results to Web search queries are ranked using heuristics that typically analyze the global link topology, user behavior, and content relevance. We point to a particular inefficiency of such methods: information redundancy. In queries where learning about a subject is an objective, modern search engines return relatively unsatisfactory results as they consider the query coverage by each page individually, not a set of pages as a whole. We address this problem using essential pages. If we denote as $\mathbb{S}_Q$ the total knowledge that exists on the Web about a given query $Q$, we want to build a search engine that returns a set of essential pages $E_Q$ that maximizes the information covered over $\mathbb{S}_Q$. We present a preliminary prototype that optimizes the selection of essential pages; we draw some informal comparisons with respect to existing search engines; and finally, we evaluate our prototype using a blind-test user study.
['Ashwin Swaminathan', 'Cherian V. Mathew', 'Darko Kirovski']
Essential Pages